Collaboration Networks, Structural Holes, and Innovation- A Longitudinal Study

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计算机其它_Collaboration network in science of networks(网络科学领域科学家合作关系)

Collaboration network in science of networks(网络科学领域科学家合作关系)数据摘要：The file NetScience contains a coauthorship network of scientists working on network theory and experiment, as compiled by M. Newman in May 2006. The network was compiled from the bibliographies of two review articles on networks, M.E.J. Newman, SIAM Review 45, 167-256 (2003) and S. Boccaletti et al., Physics Reports 424, 175-308 (2006), with a few additional references added by hand. The version given here contains all components of the network, for a total of 1589 scientists, and not just the largest component of 379 scientists previously published. The network is weighted, with weights assigned as described in M.E.J. Newman, Phys. Rev. E 64, 016132 (2001).中文关键词：合作关系,网络,科学家,网络理论,实验,英文关键词：coauthorship,network,scientists,network theory,experiment,数据格式：TEXT数据用途：研究网络结构等数据详细介绍：NetScience Collaboration network in science of networksDataset NetScienceDescription valued undirected network with 1589 vertices and 2742 edges; author X wrote a joint work with author Y; value is the MEJ Newman weight. BackgroundDownloaded from M.E.J. Newman's data pageThe file NetScience contains a coauthorship network of scientists working on network theory and experiment, as compiled by M. Newman in May 2006. The network was compiled from the bibliographies of two review articles on networks, M.E.J. Newman, SIAM Review 45, 167-256 (2003) and S. Boccaletti et al., Physics Reports 424, 175-308 (2006), with a few additional references added by hand. The version given here contains all components of the network,for a total of 1589 scientists, and not just the largest component of 379 scientists previously published. The network is weighted, with weights assigned as described in M.E.J. Newman, Phys. Rev. E 64, 016132 (2001).If you make use of these data, please cite M.E.J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprintphysics/0605087 (2006).HistoryScience compiled by M. Newman in May 2006;Science.gml by Mark Newman on Sat Jul 22 06:24:59 2006;3.Transformed into Pajek format by Vladimir Batagelj, March 1, 2007. References1.M.E.J. Newman (2003): The structure and function of complex networks,SIAM Review 45, 167-256 .2.S. Boccaletti et al. (2006); , Complex networks: Structure and dynamicsPhysics Reports 424, 175-308.3.M.E.J. Newman (2006): Finding community structure in networks usingthe eigenvectors of matrices, Preprint physics/0605087.数据预览：点此下载完整数据集。

资讯科技专有名词中英对照

资讯科技专有名词中英对照
以下是一些常见的资讯科技专有名词的中英对照：
1. Artificial Intelligence (AI) 人工智能
2. Machine Learning 机器学习
3. Internet of Things (IoT) 物联网
4. Cloud Computing 云计算
5. Big Data 大数据
6. Virtual Reality (VR) 虚拟现实
7. Augmented Reality (AR) 增强现实
8. Blockchain 区块链
9. Cryptocurrency 加密货币
10. Cybersecurity 网络安全
11. User Interface (UI) 用户界面
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12. User Experience (UX) 用户体验
13. Algorithm 算法
14. Data Mining 数据挖掘
15. Robotics 机器人技术
16. 3D Printing 三维打印
17. Biometric Authentication 生物特征认证
18. Quantum Computing 量子计算
19. Internet Protocol (IP) 互联网协议
20. API (Application Programming Interface) 应用程序接口
这些只是其中的一些例子，资讯科技领域的专有名词非常丰富，还有许多其他的术语和缩写词。

2。

国际科学合作领域研究的国家合作网络图谱分析

国际科学合作领域研究的国家合作网络图谱分析侯剑华【摘要】在美国科技情报研究所(ISI)的引文索引数据库中,检索科学合作领域研究相关的文献数据,通过CiteSpace信息可视化软件绘制该领域国家合作-主题词混合网络知识图谱,对国际科学合作领域的国家合作情况进行可视化分析,揭示当前国际科学合作领域研究的国家地域分布,探测科学合作领域的主要研究力量布局；分析中国科学合作领域研究的研究热点和研究前沿问题以及中国在世界科学合作领域研究中的地位和作用.%This paper collected the data on scientific collaboration from web of cience of ISI, U. S. A. Then it drew coun-tries collaboration - burst terms mixed citation network by CiteSpace software, in which we can analysis the countries col-laboration of scientific collaboration research. It showed the countries and regions distribution of scientific collaboration and research collaboration. At the same time, the paper detected the research focus and research fronts of scientific collabora-tion in China and its level and effect in international scientific collaboration research domain.【期刊名称】《科技管理研究》【年(卷),期】2012(032)009【总页数】4页(P18-21)【关键词】科学合作;信息可视化;合作网络;CiteSpace;科学计量【作者】侯剑华【作者单位】大连大学人文学部,辽宁大连116622【正文语种】中文【中图分类】G301科学合作已经成为科学研究中的一个重要方面，这源于科学发展的复杂性、技术的飞速变化和知识的动态增长以及高度发展的专门知识和技能，个体科学家通常不能支付全部的昂贵试验耗费和研究资源，必须通过合作来解决复杂的科学研究问题[1]。

信息技术常用术语中英文对照表

信息技术常用术语中英文对照表1. 互联网 (Internet)2. 网络安全 (Cybersecurity)3. 云计算 (Cloud Computing)5. 大数据 (Big Data)6. 机器学习 (Machine Learning)7. 物联网 (Internet of Things)8. 虚拟现实 (Virtual Reality)9. 增强现实 (Augmented Reality)10. 数字化转型 (Digital Transformation)11. 数据挖掘 (Data Mining)12. 信息安全 (Information Security)13. 信息技术 (Information Technology)15. 服务器 (Server)16. 客户端 (Client)17. 网络协议 (Network Protocol)18. 软件开发 (Software Development)19. 数据库 (Database)20. 编程语言 (Programming Language)21. 操作系统 (Operating System)22. 硬件 (Hardware)23. 软件 (Software)24. 网络基础设施 (Network Infrastructure)26. 数字营销 (Digital Marketing)27. 网络攻击 (Cyber Attack)28. 数据加密 (Data Encryption)29. 信息架构 (Information Architecture)30. 网络安全漏洞 (Cybersecurity Vulnerability)31. 信息系统 (Information System)32. 网络安全策略 (Cybersecurity Strategy)33. 网络安全意识 (Cybersecurity Awareness)34. 数字化战略 (Digital Strategy)35. 网络安全法规 (Cybersecurity Regulation)36. 信息安全标准 (Information Security Standard)37. 网络安全解决方案 (Cybersecurity Solution)38. 网络安全威胁 (Cybersecurity Threat)39. 信息安全事件 (Information Security Incident)40. 网络安全审计 (Cybersecurity Audit)41. 信息安全风险管理 (Information Security Risk Management)42. 网络安全监控 (Cybersecurity Monitoring)43. 信息安全培训 (Information Security Training)44. 网络安全事件响应 (Cybersecurity Incident Response)45. 信息安全政策 (Information Security Policy)46. 网络安全评估 (Cybersecurity Assessment)47. 信息安全意识提升 (Information Security Awareness)48. 网络安全培训 (Cybersecurity Training)49. 信息安全策略 (Information Security Strategy)50. 网络安全管理体系 (Cybersecurity Management System)信息技术常用术语中英文对照表51. 网络服务 (Network Service)52. 数据传输 (Data Transmission)53. 信息架构 (Information Architecture)54. 信息安全审计 (Information Security Audit)55. 信息安全认证 (Information Security Certification)56. 信息安全管理体系 (Information Security Management System)57. 信息安全策略 (Information Security Strategy)58. 信息安全培训 (Information Security Training)59. 信息安全意识 (Information Security Awareness)60. 信息安全风险管理 (Information Security Risk Management)61. 信息安全事件 (Information Security Incident)62. 信息安全标准 (Information Security Standard)63. 信息安全法规 (Information Security Regulation)64. 信息安全解决方案 (Information Security Solution)65. 信息安全威胁 (Information Security Threat)66. 信息安全监控 (Information Security Monitoring)67. 信息安全评估 (Information Security Assessment)68. 信息安全政策 (Information Security Policy)69. 信息安全审计 (Information Security Audit)70. 信息安全认证 (Information Security Certification)71. 信息安全管理体系 (Information Security Management System)72. 信息安全策略 (Information Security Strategy)73. 信息安全培训 (Information Security Training)74. 信息安全意识 (Information Security Awareness)75. 信息安全风险管理 (Information Security Risk Management)76. 信息安全事件 (Information Security Incident)77. 信息安全标准 (Information Security Standard)78. 信息安全法规 (Information Security Regulation)79. 信息安全解决方案 (Information Security Solution)80. 信息安全威胁 (Information Security Threat)81. 信息安全监控 (Information Security Monitoring)82. 信息安全评估 (Information Security Assessment)83. 信息安全政策 (Information Security Policy)84. 信息安全审计 (Information Security Audit)85. 信息安全认证 (Information Security Certification). 信息安全管理体系 (Information Security Management System)87. 信息安全策略 (Information Security Strategy)88. 信息安全培训 (Information Security Training)89. 信息安全意识 (Information Security Awareness)Management)91. 信息安全事件 (Information Security Incident)92. 信息安全标准 (Information Security Standard)93. 信息安全法规 (Information Security Regulation)94. 信息安全解决方案 (Information Security Solution)95. 信息安全威胁 (Information Security Threat)96. 信息安全监控 (Information Security Monitoring)97. 信息安全评估 (Information Security Assessment)98. 信息安全政策 (Information Security Policy)99. 信息安全审计 (Information Security Audit)100. 信息安全认证 (Information Security Certification)信息技术常用术语中英文对照表51. 网络服务 (Network Service)52. 数据传输 (Data Transmission)53. 信息架构 (Information Architecture)54. 信息安全审计 (Information Security Audit)55. 信息安全认证 (Information Security Certification)56. 信息安全管理体系 (Information Security Management System)57. 信息安全策略 (Information Security Strategy)58. 信息安全培训 (Information Security Training)59. 信息安全意识 (Information Security Awareness)Management)61. 信息安全事件 (Information Security Incident)62. 信息安全标准 (Information Security Standard)63. 信息安全法规 (Information Security Regulation)64. 信息安全解决方案 (Information Security Solution)65. 信息安全威胁 (Information Security Threat)66. 信息安全监控 (Information Security Monitoring)67. 信息安全评估 (Information Security Assessment)68. 信息安全政策 (Information Security Policy)69. 信息安全审计 (Information Security Audit)70. 信息安全认证 (Information Security Certification)71. 信息安全管理体系 (Information Security Management System)72. 信息安全策略 (Information Security Strategy)73. 信息安全培训 (Information Security Training)74. 信息安全意识 (Information Security Awareness)75. 信息安全风险管理 (Information Security Risk Management)76. 信息安全事件 (Information Security Incident)77. 信息安全标准 (Information Security Standard)78. 信息安全法规 (Information Security Regulation)79. 信息安全解决方案 (Information Security Solution)80. 信息安全威胁 (Information Security Threat)81. 信息安全监控 (Information Security Monitoring)82. 信息安全评估 (Information Security Assessment)83. 信息安全政策 (Information Security Policy)84. 信息安全审计 (Information Security Audit)85. 信息安全认证 (Information Security Certification). 信息安全管理体系 (Information Security Management System)87. 信息安全策略 (Information Security Strategy)88. 信息安全培训 (Information Security Training)89. 信息安全意识 (Information Security Awareness)90. 信息安全风险管理 (Information Security Risk Management)91. 信息安全事件 (Information Security Incident)92. 信息安全标准 (Information Security Standard)93. 信息安全法规 (Information Security Regulation)94. 信息安全解决方案 (Information Security Solution)95. 信息安全威胁 (Information Security Threat)96. 信息安全监控 (Information Security Monitoring)97. 信息安全评估 (Information Security Assessment)98. 信息安全政策 (Information Security Policy)99. 信息安全审计 (Information Security Audit)100. 信息安全认证 (Information Security Certification) 101. 数据库管理系统 (Database Management System)102. 编程语言 (Programming Language)103. 硬件 (Hardware)104. 软件 (Software)105. 操作系统 (Operating System) 106. 服务器 (Server)107. 客户端 (Client)108. 网络协议 (Network Protocol) 109. 软件开发 (Software Development) 110. 数据库 (Database)111. 编程语言 (Programming Language) 112. 操作系统 (Operating System) 113. 硬件 (Hardware)114. 软件 (Software)115. 服务器 (Server)116. 客户端 (Client)117. 网络协议 (Network Protocol) 118. 软件开发 (Software Development) 119. 数据库 (Database)120. 编程语言 (Programming Language) 121. 操作系统 (Operating System) 122. 硬件 (Hardware)123. 软件 (Software)124. 服务器 (Server)125. 客户端 (Client)126. 网络协议 (Network Protocol)127. 软件开发 (Software Development) 128. 数据库 (Database)129. 编程语言 (Programming Language) 130. 操作系统 (Operating System) 131. 硬件 (Hardware)132. 软件 (Software)133. 服务器 (Server)134. 客户端 (Client)135. 网络协议 (Network Protocol) 136. 软件开发 (Software Development) 137. 数据库 (Database)138. 编程语言 (Programming Language) 139. 操作系统 (Operating System) 140. 硬件 (Hardware)141. 软件 (Software)142. 服务器 (Server)143. 客户端 (Client)144. 网络协议 (Network Protocol) 145. 软件开发 (Software Development) 146. 数据库 (Database)147. 编程语言 (Programming Language) 148. 操作系统 (Operating System) 149. 硬件 (Hardware)150. 软件 (Software)。

网络的分类名词解释英文

网络的分类名词解释英文随着科技的进步和互联网的普及，网络已经成为人们日常生活中不可或缺的一部分。

网络不仅仅是人们获取信息、交流和娱乐的工具，也是连接全球的桥梁。

不同类型的网络在实现不同功能和目标方面有着不同的用途和命名分类。

本文将介绍一些常见的网络分类，并提供相关英文解释。

1. 局域网（Local Area Network，缩写为LAN）局域网是指在一个较小范围内以计算机网络为基础建立的网络。

它主要用于办公室、学校和家庭等局限空间内的计算机之间的数据传输和资源共享。

LAN通常由一组相互连接的计算机、服务器、打印机和其他网络设备组成。

它可以通过有线或无线连接来实现，并通常由路由器或交换机进行控制和管理。

2. 广域网（Wide Area Network，缩写为WAN）广域网是指跨越较大地理范围的计算机网络。

它可以连接不同城市、国家甚至是全球范围内的计算机和网络设备。

WAN通过使用公共或专用的传输线路、卫星链路或电话网络来连接不同地点的计算机。

常见的例子包括互联网和公司间的专用网络。

3. 无线局域网（Wireless Local Area Network，缩写为WLAN）无线局域网是指使用无线通信技术构建的局域网。

它使用无线电波或红外线信号来传输数据，使得设备可以无线连接到局域网并进行通信。

WLAN通常由无线路由器、无线适配器和移动设备组成。

它广泛应用于家庭、办公室、机场、图书馆等场所，为用户提供无线上网和移动性。

4. 全球性网络（Internet）全球性网络，即互联网，是连接世界上所有计算机网络的网络。

它是一个开放的、去中心化的网络，使全球各地的计算机能够互相通信和传输数据。

互联网使用标准的互联网协议（TCP/IP）进行数据传输，通过因特网服务提供商（ISP）连接到互联网。

它提供了无限的资源和信息，涵盖了各种服务和应用，如电子邮件、搜索引擎、社交媒体和在线购物等。

5. 虚拟专用网（Virtual Private Network，缩写为VPN）虚拟专用网是一种通过公共网络（如互联网）创建的安全连接。

合作网络结构洞对企业技术创新能力的影响研究——以我国集成电路产业为例

合作网络结构洞对企业技术创新能力的影响研究——以我国集成电路产业为例罗鄂湘;韩丹丹【摘要】To investigate the influence of the location of the structural holes in the cooperative network on the technological innovation capability of the enterprises and explore whether technological diversification plays a moderating role, this paper makes use of cooperative patent data from 2006 to 2015 years of the enterprises in the IC industry in China to build the cooperation network among the enterprises in the IC industry of our country. On this basis, the negative two item regression model is used to study the relationship between structural holes, technological diversification and technological innovation capability of enterprises. The results show that there is an inverted U relation between structural holes and technological innovation capability of enterprises, and techno-logical diversification negatively regulates the influence of structural holes on technological innovation capability of enterprises. Toac-ertainextent, some practical suggestions are put forward for enterprises in IC industry to improve their technological innovation capa-bility by using cooperation networks.%为研究企业在合作网络中占据结构洞的位置对企业技术创新能力的影响,并探究技术多元化在其中是否发挥调节作用,本文利用我国集成电路产业企业在2006~2015年的合作专利数据,构建我国集成电路产业企业间的合作网络,在此基础上采用负二项回归模型研究结构洞、技术多元化和企业技术创新能力之间的关系.研究结果表明结构洞与企业技术创新能力之间存在着倒U形关系,技术多元化负向调节结构洞对企业技术创新能力的影响.本文在一定程度上为我国集成电路产业企业利用合作网络提高其技术创新能力提出了实践性的建议.【期刊名称】《工业技术经济》【年(卷),期】2018(037)003【总页数】7页(P44-50)【关键词】集成电路产业;合作网络;结构洞;技术多元化;技术创新能力;负二项回归模型【作者】罗鄂湘;韩丹丹【作者单位】上海理工大学管理学院,上海 200093;上海理工大学管理学院,上海200093【正文语种】中文【中图分类】F273.1;F224引言1992年，美国社会学家Burt以弱关系理论、网络中心度概念和独特交换伙伴权利等理论为基础，在《结构洞：竞争的社会结构》一书中提出了结构洞理论。

大规模复杂网络的建模与分析

大规模复杂网络的建模与分析随着信息技术的不断发展，大规模复杂网络（Large-scale Complex Networks）在各个领域的应用越来越广泛。

从社交媒体到物联网，从生物网络到交通网络，这些网络既包含了大量的节点和边，又表现出复杂的拓扑结构和动态行为。

建模和分析这些网络有助于我们理解网络的性质和行为，并从中发现隐藏的模式和结构。

在建模大规模复杂网络时，一个常见的方法是使用图论来描述网络的结构。

图论是一种数学工具，用于研究节点和边之间的关系。

网络中的节点可以代表人、物体、事件或其他实体，边可以代表节点之间的关联、连接或交互。

通过将网络转化为图，我们可以利用图论的方法来量化网络的特性，并推导出关于网络结构的定量规律。

在对大规模复杂网络进行建模时，我们可以使用不同的图模型来描述不同的网络特性。

例如，无标度网络模型可以用来描述具有幂律度分布的节点度分布的网络。

这种分布意味着只有少数节点具有极大的度，而大多数节点具有较小的度。

这种模型可以帮助我们理解为什么在一些网络中，一些节点具有巨大的影响力，而其他节点则相对较弱。

另一个常用的图模型是小世界网络模型。

这种模型在描述社交网络、互联网和其他社会系统时特别有用。

小世界网络中，大部分节点与其他节点有较短的路径相连。

这种结构使得信息能够迅速传播，并且网络的全局特性可以通过仅观察少数节点即可获得。

小世界网络模型可以帮助我们理解为什么在一些网络中，信息传播非常迅速，以及如何在这些网络中更有效地传播和传递信息。

除了图模型，我们还可以使用其他建模方法来描述大规模复杂网络。

例如，动力学模型可以用来描述网络中节点的状态和行为的演化过程。

这些模型通常基于节点之间的相互作用和信息传递，可以帮助我们预测网络中节点的行为和状态的变化。

另外，排队论模型可以用来描述网络中资源的分配和利用情况。

通过对网络中节点之间的需求和资源供应进行建模，我们可以探索如何优化资源分配以最大化网络的效率和性能。

科技与互联网相关单词

科技与互联网相关单词科技与互联网的快速发展和普及，给人们的生活带来了巨大的变化。

以下是一些与科技和互联网相关的常用单词，让我们一起来了解它们吧。

1. 人工智能（Artificial Intelligence，简称AI）人工智能是指机器能够模拟人类的智能行为的一门科学技术。

近年来，人工智能在图像识别、自然语言处理等领域取得了重大突破，被广泛应用于各个行业。

2. 大数据（Big Data）大数据是指处理规模庞大、多样化的数据集的技术和方法。

随着互联网的快速发展，海量数据的产生和存储已经成为一项巨大的挑战，大数据技术的运用能够帮助人们从数据中获取有价值的信息。

3. 云计算（Cloud Computing）云计算是指通过网络提供计算资源和服务的一种方式。

通过云计算，用户可以根据自身需求随时获得必要的计算资源，无需投资大量的硬件设备，大幅降低了成本。

4. 虚拟现实（Virtual Reality，简称VR）虚拟现实是指利用计算机生成的模拟环境来产生一种虚拟的感觉和体验。

VR技术已经广泛应用于游戏、教育、医疗等领域，为人们提供了一种身临其境的感觉。

5. 物联网（Internet of Things，简称IoT）物联网是指通过互联网连接的各种物理设备和对象之间的互联网系统。

物联网的发展使电子设备能够相互交流和共享数据，为人们的生活和工作带来了巨大的便利。

6. 加密货币（Cryptocurrency）加密货币是指以加密技术为基础的数字货币，如比特币、以太坊等。

相比传统货币，加密货币具有去中心化、匿名性等特点，其发展开辟了全新的支付和投资方式。

7. 区块链（Blockchain）区块链是一种分布式数据库技术，可以记录和存储交易数据，具备去中心化、安全性高等特点，广泛用于加密货币的交易和管理上。

8. 人脸识别（Facial Recognition）人脸识别是一种通过计算机对人脸进行识别和分析的技术。

人脸识别技术已经应用于人脸支付、门禁系统等领域，提升了生活的便利性和安全性。

collaboration networks 数据集解析

collaboration networks 数据集解析1. 引言1.1 概述本篇文章将对Collaboration Networks数据集进行解析和分析。

Collaboration Networks是指通过合作关系构建的网络，可以用来研究人与人、团队与团队之间的协作行为。

随着社交媒体、科学研究、商业合作等领域的快速发展，对于协作网络的研究和分析变得越来越重要。

1.2 背景在各个领域中，人与人之间的协作关系成为推动创新和发展的重要因素。

通过研究合作网络，我们可以了解到哪些实体或团体更有可能形成协作关系，这对于优化资源配置、提高效率和创造价值具有重要意义。

因此，深入探索Collaboration Networks数据集，揭示其中隐藏的规律和模式，对于我们更好地理解和应用协作行为具有重要意义。

1.3 目的本文旨在介绍Collaboration Networks数据集并分享其在各个领域中的应用。

首先，我们将概述这一数据集并解释其结构以及相关信息。

接下来，我们将探讨数据预处理与清洗技术，并讨论如何处理缺失值和转换数据格式。

随后，我们将介绍网络分析工具，并解释如何利用这些工具进行关键指标的分析和解读。

最后，我们将展示一些可视化技术的应用案例，并总结分析结果。

通过本文的撰写，我们希望能够为相关领域的研究者和从业者提供一个全面的了解Collaboration Networks数据集和相关技术应用的参考指南。

2. Collaboration Networks 数据集介绍2.1 数据集概述Collaboration Networks 数据集是一种收集和记录合作关系的数据集，广泛应用于社会网络分析、学术研究以及企业管理等领域。

该数据集描述了个体之间的相互作用，例如人与人之间的合作、组织与组织之间的关联等。

通过收集并分析这些合作关系，我们可以揭示出合作网络中的模式和动态变化，并从中获得有价值的洞察。

2.2 数据结构分析Collaboration Networks 数据集通常采用图结构表示，其中节点表示实体（如人、组织或其他个体），边表示实体之间的合作关系。

互联网社区优缺点英语作文

The Internet has revolutionized the way we communicate and interact with each other.Online communities have become an integral part of our digital lives,offering platforms for individuals to connect,share,and learn from one another.However,like any tool,the Internet community has its pros and cons.Lets delve into the advantages and disadvantages of Internet communities in English.Advantages of Internet Communities1.Global Connectivity:Internet communities allow individuals from different parts of the world to connect and interact.This global connectivity fosters cultural exchange and understanding,breaking down geographical barriers.2.Knowledge Sharing:Online platforms provide a space for the sharing of knowledge and expertise.Whether its through forums,blogs,or social media,individuals can learn from each others experiences and gain insights into various fields.3.Support Networks:For those facing challenges or seeking advice,Internet communities can offer support and camaraderie.People with similar interests or experiences can find solace and advice in these virtual spaces.4.Collaboration Opportunities:The Internet facilitates collaboration on a scale that was previously munities can work together on projects,share resources, and contribute to collective goals.5.Accessibility:Internet communities are accessible24/7,allowing individuals to engage at their convenience.This flexibility is particularly beneficial for those with busy schedules or those living in remote areas.6.Innovation and Creativity:The Internet is a breeding ground for new ideas and munities can inspire and challenge each other,leading to the development of innovative solutions and creative works.Disadvantages of Internet Communities1.Privacy Concerns:The digital nature of Internet communities can lead to privacy issues.Personal information can be inadvertently shared or deliberately exploited,leading to potential misuse.2.Cyberbullying:Unfortunately,the anonymity provided by the Internet can also be a breeding ground for cyberbullying.Individuals may face harassment or abuse withinthese communities.3.Misinformation:The ease of sharing information online can lead to the spread of misinformation or fake news.Without proper verification,communities can be misled or influenced by inaccurate data.4.Isolation:While Internet communities can connect people globally,they can also lead to a sense of isolation.Overreliance on virtual interactions can detract from facetoface relationships and community engagement.5.Time Consumption:The allure of online communities can be addictive,leading to excessive screen time and a neglect of reallife responsibilities and social interactions.mercialization:Many Internet communities are not immune to commercial pressures.Advertisements and sponsored content can sometimes overshadow the communitys original purpose,leading to a loss of authenticity.7.Echo Chambers:Internet communities can sometimes become echo chambers,where individuals are only exposed to opinions that align with their own.This can limit exposure to diverse perspectives and foster a sense of groupthink.In conclusion,Internet communities offer a myriad of benefits,from fostering global connections to providing platforms for creativity and collaboration.However,they also present challenges such as privacy concerns,misinformation,and the potential for isolation.As users of these digital spaces,it is crucial to be aware of these pros and cons and to engage in online communities with a critical and mindful approach.。

Modularity and community structure in networks

a r X i v :p h y s i c s /0602124v 1 [p h y s i c s .d a t a -a n ] 17 F eb 2006Modularity and community structure in networksM. E.J.NewmanDepartment of Physics and Center for the Study of Complex Systems,Randall Laboratory,University of Michigan,Ann Arbor,MI 48109–1040Many networks of interest in the sciences,including a variety of social and biological networks,are found to divide naturally into communities or modules.The problem of detecting and characterizing this community structure has attracted considerable recent attention.One of the most sensitive detection methods is optimization of the quality function known as “modularity”over the possible divisions of a network,but direct application of this method using,for instance,simulated annealing is computationally costly.Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network,which we call the modularity matrix,and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times.We demonstrate the algorithm with applications to several network data sets.IntroductionMany systems of scientiﬁc interest can be represented as networks—sets of nodes or vertices joined in pairs by lines or edges .Examples include the Internet and the worldwide web,metabolic networks,food webs,neural networks,communication and distribution networks,and social networks.The study of networked systems has a history stretching back several centuries,but it has expe-rienced a particular surge of interest in the last decade,especially in the mathematical sciences,partly as a result of the increasing availability of large-scale accurate data describing the topology of networks in the real world.Statistical analyses of these data have revealed some un-expected structural features,such as high network tran-sitivity [1],power-law degree distributions [2],and the existence of repeated local motifs [3];see [4,5,6]for reviews.One issue that has received a considerable amount of attention is the detection and characterization of com-munity structure in networks [7,8],meaning the appear-ance of densely connected groups of vertices,with only sparser connections between groups (Fig.1).The abil-ity to detect such groups could be of signiﬁcant practical importance.For instance,groups within the worldwide web might correspond to sets of web pages on related top-ics [9];groups within social networks might correspond to social units or communities [10].Merely the ﬁnding that a network contains tightly-knit groups at all can convey useful information:if a metabolic network were divided into such groups,for instance,it could provide evidence for a modular view of the network’s dynamics,with dif-ferent groups of nodes performing diﬀerent functions with some degree of independence [11,12].Past work on methods for discovering groups in net-works divides into two principal lines of research,both with long histories.The ﬁrst,which goes by the name of graph partitioning ,has been pursued particularly in computer science and related ﬁelds,with applications in parallel computing and VLSI design,among other ar-eas [13,14].The second,identiﬁed by names such as blockFIG.1:The vertices in many networks fall naturally into groups or communities,sets of vertices (shaded)within which there are many edges,with only a smaller number of edges between vertices of diﬀerent groups.modeling ,hierarchical clustering ,or community structure detection ,has been pursued by sociologists and more re-cently also by physicists and applied mathematicians,with applications especially to social and biological net-works [7,15,16].It is tempting to suggest that these two lines of re-search are really addressing the same question,albeit by somewhat diﬀerent means.There are,however,impor-tant diﬀerences between the goals of the two camps that make quite diﬀerent technical approaches desirable.A typical problem in graph partitioning is the division of a set of tasks between the processors of a parallel computer so as to minimize the necessary amount of interprocessor communication.In such an application the number of processors is usually known in advance and at least an approximate ﬁgure for the number of tasks that each pro-cessor can handle.Thus we know the number and size of the groups into which the network is to be split.Also,the goal is usually to ﬁnd the best division of the network re-gardless of whether a good division even exists—there is little point in an algorithm or method that fails to divide the network in some cases.Community structure detection,by contrast,is per-2haps best thought of as a data analysis technique used to shed light on the structure of large-scale network datasets,such as social networks,Internet and web data, or biochemical munity structure meth-ods normally assume that the network of interest divides naturally into subgroups and the experimenter’s job is to ﬁnd those groups.The number and size of the groups is thus determined by the network itself and not by the experimenter.Moreover,community structure methods may explicitly admit the possibility that no good division of the network exists,an outcome that is itself considered to be of interest for the light it sheds on the topology of the network.In this paper our focus is on community structure de-tection in network datasets representing real-world sys-tems of interest.However,both the similarities and diﬀerences between community structure methods and graph partitioning will motivate many of the develop-ments that follow.The method of optimal modularity Suppose then that we are given,or discover,the struc-ture of some network and that we wish to determine whether there exists any natural division of its vertices into nonoverlapping groups or communities,where these communities may be of any size.Let us approach this question in stages and focus ini-tially on the problem of whether any good division of the network exists into just two communities.Perhaps the most obvious way to tackle this problem is to look for divisions of the vertices into two groups so as to mini-mize the number of edges running between the groups. This“minimum cut”approach is the approach adopted, virtually without exception,in the algorithms studied in the graph partitioning literature.However,as discussed above,the community structure problem diﬀers crucially from graph partitioning in that the sizes of the commu-nities are not normally known in advance.If community sizes are unconstrained then we are,for instance,at lib-erty to select the trivial division of the network that puts all the vertices in one of our two groups and none in the other,which guarantees we will have zero intergroup edges.This division is,in a sense,optimal,but clearly it does not tell us anything of any worth.We can,if we wish,artiﬁcially forbid this solution,but then a division that puts just one vertex in one group and the rest in the other will often be optimal,and so forth.The problem is that simply counting edges is not a good way to quantify the intuitive concept of commu-nity structure.A good division of a network into com-munities is not merely one in which there are few edges between communities;it is one in which there are fewer than expected edges between communities.If the num-ber of edges between two groups is only what one would expect on the basis of random chance,then few thought-ful observers would claim this constitutes evidence of meaningful community structure.On the other hand,if the number of edges between groups is signiﬁcantly less than we expect by chance—or equivalently if the number within groups is signiﬁcantly more—then it is reasonable to conclude that something interesting is going on. This idea,that true community structure in a network corresponds to a statistically surprising arrangement of edges,can be quantiﬁed using the measure known as modularity[17].The modularity is,up to a multiplicative constant,the number of edges falling within groups mi-nus the expected number in an equivalent network with edges placed at random.(A precise mathematical formu-lation is given below.)The modularity can be either positive or negative,with positive values indicating the possible presence of com-munity structure.Thus,one can search for community structure precisely by looking for the divisions of a net-work that have positive,and preferably large,values of the modularity[18].The evidence so far suggests that this is a highly eﬀective way to tackle the problem.For instance, Guimer`a and Amaral[12]and later Danon et al.[8]op-timized modularity over possible partitions of computer-generated test networks using simulated annealing.In di-rect comparisons using standard measures,Danon et al. found that this method outperformed all other methods for community detection of which they were aware,in most cases by an impressive margin.On the basis of con-siderations such as these we consider maximization of the modularity to be perhaps the deﬁnitive current method of community detection,being at the same time based on sensible statistical principles and highly eﬀective in practice.Unfortunately,optimization by simulated annealing is not a workable approach for the large network problems facing today’s scientists,because it demands too much computational eﬀort.A number of alternative heuris-tic methods have been investigated,such as greedy algo-rithms[18]and extremal optimization[19].Here we take a diﬀerent approach based on a reformulation of the mod-ularity in terms of the spectral properties of the network of interest.Suppose our network contains n vertices.For a par-ticular division of the network into two groups let s i=1 if vertex i belongs to group1and s i=−1if it belongs to group2.And let the number of edges between ver-tices i and j be A ij,which will normally be0or1,al-though larger values are possible in networks where mul-tiple edges are allowed.(The quantities A ij are the el-ements of the so-called adjacency matrix.)At the same time,the expected number of edges between vertices i and j if edges are placed at random is k i k j/2m,where k i and k j are the degrees of the vertices and m=14m ijA ij−k i k j4m s T Bs,(1)where s is the vector whose elements are the s i.The leading factor of1/4m is merely conventional:it is in-cluded for compatibility with the previous deﬁnition of modularity[17].We have here deﬁned a new real symmetric matrix B with elementsk i k jB ij=A ij−FIG.2:Application of our eigenvector-based method to the “karate club”network of Ref.[23].Shapes of vertices indi-cate the membership of the corresponding individuals in the two known factions of the network while the dotted line indi-cates the split found by the algorithm,which matches the fac-tions exactly.The shades of the vertices indicate the strength of their membership,as measured by the value of the corre-sponding element of the eigenvector.groups,but to place them on a continuous scale of“how much”they belong to one group or the other.As an example of this algorithm we show in Fig.2the result of its application to a famous network from the so-cial science literature,which has become something of a standard test for community detection algorithms.The network is the“karate club”network of Zachary[23], which shows the pattern of friendships between the mem-bers of a karate club at a US university in the1970s. This example is of particular interest because,shortly after the observation and construction of the network, the club in question split in two as a result of an inter-nal dispute.Applying our eigenvector-based algorithm to the network,weﬁnd the division indicated by the dotted line in theﬁgure,which coincides exactly with the known division of the club in real life.The vertices in Fig.2are shaded according to the val-ues of the elements in the leading eigenvector of the mod-ularity matrix,and these values seem also to accord well with known social structure within the club.In partic-ular,the three vertices with the heaviest weights,either positive or negative(black and white vertices in theﬁg-ure),correspond to the known ringleaders of the two fac-tions.Dividing networks into more than two communities In the preceding section we have given a simple matrix-based method forﬁnding a good division of a network into two parts.Many networks,however,contain more than two communities,so we would like to extend our method toﬁnd good divisions of networks into larger numbers of parts.The standard approach to this prob-lem,and the one adopted here,is repeated division into two:we use the algorithm of the previous sectionﬁrst to divide the network into two parts,then divide those parts,and so forth.In doing this it is crucial to note that it is not correct, afterﬁrst dividing a network in two,to simply delete the edges falling between the two parts and then apply the algorithm again to each subgraph.This is because the degrees appearing in the deﬁnition,Eq.(1),of the mod-ularity will change if edges are deleted,and any subse-quent maximization of modularity would thus maximize the wrong quantity.Instead,the correct approach is to deﬁne for each subgraph g a new n g×n g modularity matrix B(g),where n g is the number of vertices in the subgraph.The correct deﬁnition of the element of this matrix for vertices i,j isB(g)ij=A ij−k i k j2m ,(4)where k(g)i is the degree of vertex i within subgraph g and d g is the sum of the(total)degrees k i of the vertices in the subgraph.Then the subgraph modularity Q g=s T B(g)s correctly gives the additional contribution to the total modularity made by the division of this subgraph.In particular,note that if the subgraph is undivided,Q g is correctly zero.Note also that for a complete network Eq.(4)reduces to the previous deﬁnition for the modu-larity matrix,Eq.(2),since k(g)i→k i and d g→2m in that case.In repeatedly subdividing our network,an important question we need to address is at what point to halt the subdivision process.A nice feature of our method is that it provides a clear answer to this question:if there exists no division of a subgraph that will increase the modular-ity of the network,or equivalently that gives a positive value for Q g,then there is nothing to be gained by divid-ing the subgraph and it should be left alone;it is indi-visible in the sense of the previous section.This happens when there are no positive eigenvalues to the matrix B(g), and thus our leading eigenvalue provides a simple check for the termination of the subdivision process:if the lead-ing eigenvalue is zero,which is the smallest value it can take,then the subgraph is indivisible.Note,however,that while the absence of positive eigen-values is a suﬃcient condition for indivisibility,it is not a necessary one.In particular,if there are only small positive eigenvalues and large negative ones,the terms in Eq.(3)for negativeβi may outweigh those for positive.It is straightforward to guard against this possibility,how-ever:we simply calculate the modularity contribution for each proposed split directly and conﬁrm that it is greater than zero.Thus our algorithm is as follows.We construct the modularity matrix for our network andﬁnd its leading (most positive)eigenvalue and eigenvector.We divide the network into two parts according to the signs of the elements of this vector,and then repeat for each of the parts.If at any stage weﬁnd that the proposed split makes a zero or negative contribution to the total mod-5ularity,we leave the corresponding subgraph undivided. When the entire network has been decomposed into in-divisible subgraphs in this way,the algorithm ends. One immediate corollary of this approach is that all “communities”in the network are,by deﬁnition,indi-visible subgraphs.A number of authors have in the past proposed formal deﬁnitions of what a community is[9,16,24].The present method provides an alter-native,ﬁrst-principles deﬁnition of a community as an indivisible subgraph.Further techniques for modularity maximization In this section we describe brieﬂy another method we have investigated for dividing networks in two by mod-ularity optimization,which is entirely diﬀerent from our spectral method.Although not of especial interest on its own,this second method is,as we will shortly show,very eﬀective when combined with the spectral method.Let us start with some initial division of our vertices into two groups:the most obvious choice is simply to place all vertices in one of the groups and no vertices in the other.Then we proceed as follows.Weﬁnd among the vertices the one that,when moved to the other group, will give the biggest increase in the modularity of the complete network,or the smallest decrease if no increase is possible.We make such moves repeatedly,with the constraint that each vertex is moved only once.When all n vertices have been moved,we search the set of in-termediate states occupied by the network during the operation of the algorithm toﬁnd the state that has the greatest modularity.Starting again from this state,we repeat the entire process iteratively until no further im-provement in the modularity results.Those familiar with the literature on graph partitioning mayﬁnd this algo-rithm reminiscent of the Kernighan–Lin algorithm[25], and indeed the Kernighan–Lin algorithm provided the inspiration for our method.Despite its simplicity,weﬁnd that this method works moderately well.It is not competitive with the best pre-vious methods,but it gives respectable modularity val-ues in the trial applications we have made.However, the method really comes into its own when it is used in combination with the spectral method introduced ear-lier.It is a common approach in standard graph par-titioning problems to use spectral partitioning based on the graph Laplacian to give an initial broad division of a network into two parts,and then reﬁne that division us-ing the Kernighan–Lin algorithm.For community struc-ture problems weﬁnd that the equivalent joint strategy works very well.Our spectral approach based on the leading eigenvector of the modularity matrix gives an ex-cellent guide to the general form that the communities should take and this general form can then beﬁne-tuned by our vertex moving method,to reach the best possible modularity value.The whole procedure is repeated to subdivide the network until every remaining subgraph is indivisible,and no further improvement in the modular-ity is possible.Typically,theﬁne-tuning stages of the algorithm add only a few percent to theﬁnal value of the modularity, but those few percent are enough to make the diﬀerence between a method that is merely good and one that is, as we will see,exceptional.Example applicationsIn practice,the algorithm developed here gives excel-lent results.For a quantitative comparison between our algorithm and others we follow Duch and Arenas[19] and compare values of the modularity for a variety of networks drawn from the literature.Results are shown in Table I for six diﬀerent networks—the exact same six as used by Duch and Arenas.We compare mod-ularityﬁgures against three previously published algo-rithms:the betweenness-based algorithm of Girvan and Newman[10],which is widely used and has been incor-porated into some of the more popular network analysis programs(denoted GN in the table);the fast algorithm of Clauset et al.[26](CNM),which optimizes modularity using a greedy algorithm;and the extremal optimization algorithm of Duch and Arenas[19](DA),which is ar-guably the best previously existing method,by standard measures,if one discounts methods impractical for large networks,such as exhaustive enumeration of all parti-tions or simulated annealing.The table reveals some interesting patterns.Our al-gorithm clearly outperforms the methods of Girvan and Newman and of Clauset et al.for all the networks in the task of optimizing the modularity.The extremal opti-mization method on the other hand is more competitive. For the smaller networks,up to around a thousand ver-tices,there is essentially no diﬀerence in performance be-tween our method and extremal optimization;the mod-ularity values for the divisions found by the two algo-rithms diﬀer by no more than a few parts in a thousand for any given network.For larger networks,however,our algorithm does better than extremal optimization,and furthermore the gap widens as network size increases, to a maximum modularity diﬀerence of about a6%for the largest network studied.For the very large networks that have been of particular interest in the last few years, therefore,it appears that our method for detecting com-munity structure may be the most eﬀective of the meth-ods considered here.The modularity values given in Table I provide a use-ful quantitative measure of the success of our algorithm when applied to real-world problems.It is worthwhile, however,also to conﬁrm that it returns sensible divisions of networks in practice.We have given one example demonstrating such a division in Fig.2.We have also checked our method against many of the example net-works used in previous studies[10,17].Here we give two more examples,both involving network representationsmodularity Q network GN CNM DA this paper3419845311331068027519maximal value of the quantity known as modularity over possible divisions of a network.We have shown that this problem can be rewritten in terms of the eigenval-ues and eigenvectors of a matrix we call the modularity matrix,and by exploiting this transformation we have created a new computer algorithm for community de-tection that demonstrably outperforms the best previ-ous general-purpose algorithms in terms of both quality of results and speed of execution.We have applied our algorithm to a variety of real-world network data sets, including social and biological examples,showing it to give both intuitively reasonable divisions of networks and quantitatively better results as measured by the modu-larity.AcknowledgmentsThe author would like to thank Lada Adamic,Alex Arenas,and Valdis Krebs for providing network data and for useful comments and suggestions.This work was funded in part by the National Science Foundation un-der grant number DMS–0234188and by the James S. 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物联网工程英语

物联网专业英语复习第一部分单词或词组英译中（10空，共10分）汉语中译英（10空，共10分）第一单元单词actuator 执行器Cyber-Physical System (CPS)信息物理融合系统Cyberspace 网络空间device processing power 设备处理能力fibre-based network 基于光纤的网络Global Positioning System (GPS) 全球定位系统Internet of Things (IoT) 物联网Machine to Machine (M2M) 机器对机器nano-technology 纳米技术quick response (QR)-code reader QR 码阅读器radio frequency identification (RFID)无线射频识别技术RFID scanner RFID扫描仪Sensor 传感器shrinking thing 微小的物体storage capacity 存储空间tag 标签middleware中间件中间设备paradigm 范例、概念ubiquitous 普遍存在的gateway device 网关设备logistics 物流in the scenario of … 在…背景下from the point view of … 从…角度convergence 收敛、集合pervasive 普遍存在的domotics 家庭自动化e-health 电子医疗in the context 在…方面with reference to 关于，根据第二单元单词3rd-Generation (3G)第三代移动通信技术bluetooth蓝牙cloud computing云计算database数据库embedded software嵌入式软件enterprise local area network企业局域网EPC Global一个组织（产品电子代码）Fibre to the x (FTTx)光纤入户=Identity authentication身份认证implant microchip植入芯片infrared sensor红外传感器infrared technology红外技术intelligent processing智能处理IPv6一种互联网协议Japanese Ubiquitous ID日本泛在标识Location Based Service (LBS)基于位置的服务logistics management物流管理serviced-oriented面向服务的Telecommunications Management Network (TMN)电信管理网络application layer应用层business layer商业服务层perception layer感知层processing layer处理层transport layer传输层ubiquitous computing普适计算Wireless Fidelity (WiFi)一种无线局域网络技术ZigBee一种低功耗个域网协议deployment调度、部署intervention介入unprecedented空前的refinement精炼、提炼concrete具体的attribute特征、属性conform to符合、遵照e-commerce电子商务assign分配、指定、赋值diverse多种多样的connotation内涵enterprise企业、事业、进取心appropriateness适当、合适immense巨大的、无穷的magnitude大小、量级representative典型的、代表module模块literacy读写能力、文化素养ultra mobile broadband (UMB)超移动宽带mass大规模的，集中的chip芯片integrated综合的、集成的precision精度、精确、精确度reliability可靠性sensitive敏感的、易受伤害的semiconductor半导体silicon硅、硅元素thermocouple热电偶hall门厅、走廊、会堂、食堂programmable可编程的biological sensor生物传感器chemical sensor化学传感器electric current电流electrode potential电极电位integrated circuit集成电路sensor/transducer technology传感器技术sensing element敏感元件transforming circuit转换电路overload capacity过载能力physical sensor物理传感器intelligent sensor智能传感器displacement sensor位移传感器angular displacement sensor角位移传感器pressure sensor压力传感器torque sensor扭矩传感器temperature sensor温度传感器quantity量、数量voltage电压pulse脉冲acquisition获取eliminate消灭、消除volume体积breakthrough突破superconductivity超导电性magnetic磁的inferior in在…方面低劣craft工艺、手艺、太空船quantum量子interference干涉antibody抗体antigen抗原immunity免疫inspect检查、视察organism有机体、生物体hepatitis肝炎high polymer高分子聚合物thin film薄膜ceramic陶瓷adsorption吸附hydrone水分子dielectric medium电解质humidity湿度plasma等离子体polystyrene聚苯乙烯intermediary媒介物polarization极化、偏振corrosion腐蚀tele-measure遥测oxidation氧化lithography光刻diffusion扩散deposition沉淀planar process平面工艺anisotropic各项异性evaporation蒸镀sputter film溅射薄膜resonant pressure sensor谐振压力传感器sophisticated富有经验的etch蚀刻diaphragm膜片beam横梁、照射Wheatstone Bridge惠斯通电桥piezo-resistance压阻gauge计量器ion离子petroleum石油lag落后barcode条码encode编码graphic图形one-dimensional barcode一维码two-dimensional barcode二维码capacity容量disposal处理、安排algorithm算法barcode reader条码阅读器facsimile传真、复写transcript成绩单authenticate认证、鉴定photocopy复印件asymmetric非对称的cryptographic加密的tamper篡改merchandise商品track跟踪personalized个人化的reflectivity反射率recognition识别agency代理commodity商品portable便携式的execute执行impair损害pantry食品柜distinguish区分individual个人的，个别的encrypt把…加密issuing authority发行机关biometric生物识别iris minutiae虹膜特征trigger switch触发开关establish建立dynamic动态的grasp抓住exchange交换retrieve重新获取capture拍摄duplicate复制forge伪造signature签名第六单元synchronous同步的asynchronous异步的barrier障碍物proliferation扩散router路由器restriction限制seismic地震的scenario方案；情节scalability可扩展的spatially空间地topology拓扑latency延迟facilitate促进release发布thermal热的intrusion入侵coordinator协调器node节点surveillance监督base station基站access point接入点，访问点ad hoc无线自组织网络data-link layer数据链路层network topology网络拓扑peer-to-peer点对点power consumption能耗resource constraints资源受限solar panels太阳能电池版plant equipment工厂设备energy efficient高效能end device终端设备Institute of Electrical and Electronics Engineers, IEEE美国电气与电子工程师学会Micro-Electro-Mechanical Systems, MEMS微机电系统Personal Area Network, PAN个域网Wireless Sensor Network, WSN 无线传感网络缩写词展开完整形式（10空，共10分）；IoT（Internet of Things）物联网RFID（Radio Frequency Identification）无线射频识别QR-code（Quick Response Code）快速响应码GPS（Global Positioning System）全球定位系统CPS（Cyber Physical System）信息物理融合系统M2M（Machine to Machine）机器对机器HTTP（Hypertext Transfer Protocol）超文本传输协议SOAP（Simple Object Access Protocol）简单对象访问协议EPC（Electronic Product Code）电子产品码WLAN（Wireless Local Area Network）无线局域网LBS（Local Based Service）基于位置的服务GSM（Global System for Mobile Communications）全球移动通信系统DNS（Domain Name Server）域名服务器HTML（Hypertext Makeup Protocol）超文本标记语言CPU（Central Processing Unit）中央处理器单元EPROM（Erasable Programmable Read Only Memory）可擦除可编程只读存储器UHF（Ultra High Frequency）超高频第二部分完型填空（4大题，每题5空，共20分）第三部分阅读理解（2大题，每题5空，共20分）第四部分：句子翻译（5题，每题6分，共30分）（2、5、7、11可能不考，不是作业本上的）1、The main strength of the IoT idea is the high impact it will have on several aspects of everyday-life and behavior of potential users. From the point of view of a private user, the most obvious effects of the IoT introduction will be visible in both working and domestic fields. In this context, domotics, assisted living, e-health, enhanced learning are only a few examples of possibleapplication scenarios in which the new paradigm will play a leading role in the near future.物联网理念的主要强大之处在于，它对潜在用户的日常生活和行为的方方面面产生很大影响。

合作网络作文英语模板

合作网络作文英语模板英文回答：Collaborative Networks: An Essential Key to Innovation and Success。

In today's rapidly changing business landscape, organizations face unprecedented challenges and opportunities. To remain competitive and drive innovation, businesses must adapt and evolve, and one key factor that has emerged as a powerful tool in this pursuit is the formation of collaborative networks.Collaborative networks, also known as business ecosystems, are alliances or partnerships between multiple organizations that come together to share resources, knowledge, and capabilities. By leveraging the strengths of each individual entity, collaborative networks create a collective pool of expertise and resources that can be harnessed to drive innovation, improve efficiency, andreduce costs.One of the primary benefits of collaborative networks is the ability to tap into a broader range of skills and expertise. By partnering with other organizations that specialize in different areas, businesses can access knowledge and technologies that they may not have internally. This cross-fertilization of ideas and innovations can lead to breakthrough solutions and new business opportunities.Another key advantage of collaborative networks is the sharing of resources. By pooling their resources, partner organizations can spread the costs of research and development, marketing, and other business functions. This shared investment can free up financial resources for organizations to allocate towards strategic initiatives that will further drive growth.Furthermore, collaborative networks foster an environment of open innovation. By working together, partners can challenge conventional thinking, explore newideas, and develop groundbreaking products and services.The collective wisdom and diversity of perspectives withina collaborative network can spark innovation that would not be possible for individual organizations acting alone.One notable example of a successful collaborative network is the development of the iPhone by Apple. Apple partnered with numerous suppliers, including Samsung, Qualcomm, and Broadcom, to leverage their expertise in hardware, software, and semiconductors. By working together, these companies were able to create a groundbreakingproduct that has transformed the mobile phone industry.Another prominent example is the collaboration between pharmaceutical companies and research institutions in the development of new drugs. By sharing research data and collaborating on clinical trials, these partnerships have accelerated the discovery and development of life-saving treatments.The benefits of collaborative networks extend beyondthe realm of innovation and efficiency gains. They can alsoenhance an organization's agility and resilience. By building a network of trusted partners, businesses can quickly adapt to changing market conditions and respond to unforeseen events. This network can provide access to alternative supply chains, new customer segments, and support in times of crisis.However, it is important to note that forming and maintaining collaborative networks is not without challenges. Key considerations include the need for clear communication, trust-building, and the alignment of goals among partners. Additionally, intellectual property rights and data privacy must be carefully managed to ensure the fair distribution of benefits and the protection of sensitive information.In conclusion, collaborative networks offer a powerful mechanism for organizations to drive innovation, improve efficiency, and enhance their competitive advantage. By leveraging the strengths of multiple entities, these partnerships create a collective pool of expertise, resources, and perspectives that can lead to breakthroughsolutions and new business opportunities. As the business landscape continues to evolve, collaborative networks will undoubtedly play an increasingly vital role in the success and prosperity of organizations.中文回答：协作网络，创新和成功的关键。

互联新学途的英语作文

In the era of the digital revolution,the landscape of education has been transformed by the advent of the Internet.The concept of Interconnected New Learning Pathways encapsulates the innovative approaches to learning that leverage the power of the Internet and related technologies.Heres an exploration of this theme in an English essay format.Title:The Interconnected New Learning PathwaysIn recent years,the rapid development of technology has ushered in a new era of education,characterized by interconnected learning pathways that transcend traditional classroom boundaries.The Internet,as the backbone of this transformation,has redefined the way we access,process,and share knowledge.The Ubiquity of Online ResourcesOne of the most significant aspects of the new learning pathways is the wealth of online resources available to students and educators alike.From opensource educational platforms to Massive Open Online Courses MOOCs,learners can now access a vast array of materials that cater to diverse learning styles and preferences.These resources are not limited by geographical constraints,making quality education accessible to anyone with an internet connection.Personalized Learning ExperiencesThe interconnected nature of new learning pathways allows for a more personalized educational experience.Learners can tailor their learning paths according to their interests,pace,and goals.Adaptive learning technologies analyze student performance and adjust the curriculum to meet individual needs,ensuring that each student receives an education that is optimized for their success.Collaborative Learning EnvironmentsThe Internet has also facilitated collaborative learning environments where students from different parts of the world can work together on projects and share ideas.Online forums, social media groups,and virtual classrooms provide platforms for discussion and collaboration,fostering a sense of community among learners and promoting the exchange of diverse perspectives.Enhanced Teaching MethodsEducators are also benefiting from the interconnected new learning pathways.They canemploy a variety of digital tools to enhance their teaching methods,such as interactive whiteboards,educational apps,and virtual reality simulations.These tools not only make learning more engaging but also provide teachers with valuable data on student performance,enabling them to make informed decisions about their teaching strategies.Challenges and SolutionsDespite the numerous advantages,the interconnected new learning pathways also present challenges,such as ensuring equitable access to technology,safeguarding against cyberbullying,and maintaining academic integrity in online assessments.Addressing these issues requires a concerted effort from educators,policymakers,and technology providers to create a safe and inclusive learning environment.The Future of LearningAs we look to the future,the interconnected new learning pathways are set to evolve further with advancements in artificial intelligence,machine learning,and other emerging technologies.These developments will likely lead to even more personalized and immersive learning experiences,preparing students for the complex challenges of the 21st century.In conclusion,the interconnected new learning pathways represent a significant shift in the educational paradigm.By embracing the potential of the Internet and associated technologies,we can create a more inclusive,engaging,and effective learning environment that prepares students for the future.As we continue to innovate and adapt, the possibilities for education are as boundless as the digital landscape itself.。

联邦学习模型的构建与模型聚合算法研究

联邦学习模型的构建与模型聚合算法研究随着人工智能技术的迅速发展，数据的价值越来越受到重视。

然而，由于数据隐私和安全的考虑，许多用户不愿意将自己的数据集集中到单一的数据中心进行训练和模型构建。

为了解决这个问题，联邦学习应运而生。

联邦学习是一种分布式机器学习方法，它允许参与方在保护数据隐私的同时，通过合作来构建和改进机器学习模型。

在联邦学习中，模型的构建和训练过程分布在多个参与方之间。

这些参与方可以是个人设备、组织或数据中心。

每个参与方都保持着自己的数据，并且只在本地训练模型。

然后，通过模型聚合算法将各个参与方的模型参数进行聚合，生成一个全局的模型，该全局模型具有良好的性能和广泛的适用性。

在联邦学习模型的构建过程中，首先需要选择一个合适的机器学习模型作为基础模型。

常用的机器学习模型，如逻辑回归、支持向量机、神经网络等，都可以应用于联邦学习中。

选择合适的模型要考虑到模型的复杂度、计算资源消耗和模型性能等因素。

接下来，为了构建高质量的联邦学习模型，需要考虑数据的异质性和不平衡性。

在联邦学习中，每个参与方的数据分布不同，可能存在领域差异。

因此，在模型构建过程中需要采取一些策略来解决这个问题。

例如，可以使用领域自适应技术来对数据进行预处理，减少领域差异对模型的影响。

除了数据异质性问题，数据不平衡也是联邦学习中的一个挑战。

参与方的数据集可能存在类别不平衡的情况，这会影响模型的训练和性能。

为了解决这个问题，可以使用一些数据平衡方法，如重采样、过采样或欠采样等。

联邦学习的模型聚合算法是将各个参与方的模型参数进行聚合，生成全局模型的关键步骤。

常用的模型聚合算法包括平均聚合、加权聚合和梯度聚合等。

平均聚合是将各个参与方的模型参数相加然后取平均，得到全局模型的参数。

加权聚合是给每个参与方的模型参数赋予一个权重，然后加权求和得到全局模型的参数。

梯度聚合是将各个参与方的梯度进行聚合，然后更新全局模型的参数。

在选择模型聚合算法时，需要考虑参与方的可信度和贡献度。

Marvel Universe looks almost like a real social network

a r X i v :c o n d -m a t /0202174v 1 [c o n d -m a t .d i s -n n ] 11 F eb 2002Marvel Universe looks almost like a real socialnetworkR.Alberich,J.Miro-Julia,F.Rossell´o Departament de Matem`a tiques i Inform`a tica,Universitat de les Illes Balears,07071Palma de Mallorca (Spain){ricardo,joe,cesc }@ipc4.uib.es 1Introduction A recent popular topic of research in social networks has been the study of collaboration networks.In these,the vertexes (or nodes)represent people and the edges that link pairs of nodes denote the existence of some kind of collaboration between them.Their popularity stems mainly from two factors.First,they are more objective than other social networks like friendship or ﬁrst-name-knowledge networks.Their links have a deﬁnite meaning,while,for instance,the meaning of links in friendship networks is subjective and thus possibly non-homogeneous throughout.And second,the existence and availability of large databases containing all information concerning movies,baseball teams,scientiﬁc papers,and other large ﬁelds of collaboration,makes it easier to create and study these networks,while reliable friendship networks can only be raised through the intensive gathering of information bymeans of interviews.Furthermore,the databases from which collaboration networks are extracted usually contain information about the time when each collaboration has taken place.This information can be used to describe the evolution of the network and then to extract properties about how social networks grow [3,15].A well-known collaboration network is the Movie Actors network ,also dubbed the Hollywood network .In it,nodes represent actors and actresses,and a link is added between two nodes when they have jointly appeared in the same ﬁlm.All information concerning this network isaccessible at the Internet Movie Database[10],and it has been studied from a mathematical point of view[1,16,18,19].This is the basis of the popular Kevin Bacon game[17],which consists of trying to connect any given actor or actress to Kevin Bacon through the shortest possible path of collaborations inﬁlms.Scientiﬁc collaboration networks have also been thoroughly studied in the last years.In such a network,nodes represent scientists and links denote the coauthorship of a scientiﬁc piece of work contained in some database.For instance,there is the so-called Erd¨o s collaboration graph.Paul Erd¨o s was a Hungarian mathematician,dead in1996,who published over1500 papers with492coauthors,more than any other mathematician in history.The Erd¨o s col-laboration graph is the mathematicians’collaboration network around Erd¨o s himself[5,6], built up through data collected by Grossman[9].Also,Newman[12–15]has studied in detail the scientiﬁc collaboration networks corresponding to several databases,namely MEDLINE (biomedical research papers in refereed journals),SPIRES(preprints and published papers in high-energy physics),NCSTRL(preprints in computer science),and Los Alamos e-Print Archive(preprints in physics).Bar´a basi et al.[3]have studied the networks based on two databases containing articles on mathematics and neuro-science,respectively,published in relevant journals.Newman[12]argues that scientiﬁc collaboration networks are true social networks,since most pairs of scientists that have written a paper together are genuinely acquainted with one another.The social meaning of the Hollywood network is,in this sense,weaker,because it has been built up mainly through the decisions of cast directors,producers and agents, rather than the voluntary collaboration of actors.Despite these,and other,diﬀerences,all collaboration networks studied so far present the same basic features:(a)on average,every pair of nodes can be connected through a short path within the network;(b)the probability that two nodes are linked is greater if they share a neighbor;and(c)the fraction of nodes with k neighbors decays roughly as a function of the form k−τfor some positive exponent τ,with perhaps a cutoﬀfor large values of k.A network satisfying properties(a)and(b)is called a small world[18,19],and if it satisﬁes(c)then it is called scale-free[1,2].Does this similarity in features represent some profound principle in human interaction?Or, on the contrary,does any large network with some“collaboration”between nodes present these characteristics?Aﬁrst,theoretical,step in this direction has been recently made by Newman et al.[16],who have developed a theory of random collaboration networks and have shown that some statistical data of most“real-life”collaboration networks diﬀer substantially from the data obtained from random models.In this paper we want to contribute to a possible answer to these questions by analyzing a new collaboration network,that is artiﬁcial,but mimics real-life networks:the Marvel Universe collaboration network.In it,the nodes correspond to Marvel Comics characters, and two nodes are linked when the corresponding characters have jointly appeared in the same Marvel comic book.Marvel Comics,together with DC Comics,have been for many decades the two main comic book publishing companies in the world[8,11].It was founded in1939by M.Goodman, under the name of Timely Comics Inc.;it changed its name in the early1960s to Marvel Comics,which was also the name of theﬁrst comic book published by Timely.After aﬁrst decade of popularity,known as the Golden Age of comics(1939-49),and a later period ofgeneral waning of interest in super-hero stories,Marvel relaunched in1961its super-hero comic books publishing line,starting what has been known as the Marvel Age of Comics. Some of the characters created in this period,like Spider-Man,the Fantastic Four,the X-Men,together with other characters rescued from the Golden Age,like Captain America, are world-wide known and have become cultural icons of the western society of the last forty years.One of the main features of Marvel Comics from the sixties to our days has been the cre-ation and development,under the leading pen of Stan Lee,of the so-called Marvel Universe. Although crossovers(a hero with its own title series appears in an issue of another hero’s series)were not uncommon in the Golden Age period,the nature and span of the crossovers in the books from the Marvel Age led to the perception that all Marvel characters lived their adventures in the sameﬁctional cosmos,called the Marvel Universe,where they interacted like real actors.This concept was helped by the interrelation of all titles that were being created,which made characters and even plots cross over on a regular basis,by the appear-ance of the same villains and secondary characters in comic books of diﬀerent titles,and by continuous references to events that were simultaneously happening,or had happened,in other books.A paradigm of the Marvel Universe could be Quicksilver,who appearedﬁrst as a member of Magneto’s Brotherhood of Evil Mutants in the early issues of Uncanny X-Men, then he became a member of the Avengers and later of X-Factor,to end as the leader of the Knights of Wundagore;he is also the son of Magneto,the twin brother of the Scarlet Witch, and he married Crystal,a formerﬁanc´e e of Fantastic Four’s Human Torch and a member of the Inhumans(as well as of the Fantastic Four as a substitute of the Invisible Woman when she took her“maternal leave”).The Marvel Universe network captures the social structure of this Marvel Universe,because most pairs of characters that have jointly appeared in the same comic book have fought shoulder to shoulder or each other,or have had some other strong relationship,like family ties or kidnapping.Thus,it shares,in its artiﬁcial way,the true social nature of scientiﬁc collaboration networks,while the way it has grown has echoes of the Hollywood network,as writers,directors and producers create their characters and assign them to actors in a way that somewhat resembles the way Marvel writers make characters appear in comic books. Thus,besides any sentimental or cultural motive,this is where the main reason for studying the properties of the Marvel Universe lies:it is a purely artiﬁcial social network,whose nodes correspond to invented entities and whose links have been raised by a team of writers without any preconception for a period of forty years.We considered therefore interesting to know if the Marvel Universe network’s artiﬁcial nature would resemble real-life collaboration networks,or,on the contrary,would rather look like a random collaboration network.As we shall see,theﬁrst is essentially the case:most statistical data of the Marvel Universe diﬀer from the random model in a way reminiscent of real-life collaboration networks.Nevertheless, we must mention that there is one particular value,the clustering coeﬃcient,that also greatly diﬀers from what one would expect in a real-life collaboration network.We shall argue that this diﬀerence stems from the way how characters were distributed among books by Marvel writers,which is diﬀerent from the way how real-life scientists join to write scientiﬁc papers. After all,men,even Stan Lee(The Man himself)cannot imitate society.2The Marvel Universe networkWe deﬁne the Marvel Universe network(MU)as the network whose nodes are signiﬁcant Marvel characters and where two characters are linked when they jointly appear in a sig-niﬁcant way in the same comic book.We only consider here comics published after Issue1 of Fantastic Four(dated November1961),which is understood as the point of departure of the Marvel Age of Comics.Any study like this one must be based on a database,which puts the main restriction to its scope.In this case,the database we have used is the Marvel Chronology Project(MCP), which,according to its creator,R.Chappell[7],catalogs every canonical appearance by every signiﬁcant Marvel character.Thus,the“signiﬁcant characters”represented by nodes in our network and the“signiﬁcant appearances”that yield the links in it are,actually,nothing but those characters and appearances currently included in the MCP database.Nevertheless, all in all,this database collects over96000appearances by more than6500characters in about13000comic books,and thus yields quite a complete picture of the Marvel Universe. Although the MCP database is notﬁnished(it has a main gap,as it does not include comic books published between early1993and mid1994,as well as some other minor ones)we believe that this does not aﬀect in a signiﬁcant way the results obtained in our analysis.It is necessary to clarify what we understood by a character when building up MU.On the one hand,it is quite common for the same person in the Marvel Universe to take diﬀer-ent personalities.As an example,recall Hank Pym,one of the original and most popular Avengers:it has been known,in diﬀerent periods,as the Ant-Man,the Giant-Man,Goliath, YellowJacket,and has even appeared simply as the world’s greatest biochemist Dr.Henry Pym in many books.On the other hand,from time to time diﬀerent characters may assume the same personality:for instance,besides Hank Pym,there have been at least two more Goliath’s:Clint Barton(who changed from Hawkeye to Goliath,before returning back to Hawkeye)and Erik Josten(who was Power Man before becoming the third Goliath,and after that he took the name of Atlas,being actually the second character with this nick-name).In fact,these problems with the identiﬁcation of nodes are not speciﬁc to MU,but they are shared by all collaboration networks:diﬀerent authors can appear under the same name in a scientiﬁc collaboration network,and an actress could use a nickname during her period as prodigy child,then use her maiden name after adolescence,and then take her hus-bands’name after every wedding,coming back to her maiden name in every period between marriages.Fortunately,and contrary to scientiﬁc databases or the Internet Movie Database, the MCP database takes care of most vicissitudes concerning name changes.We decided then to assign a node to every“person”(or,more in accordance with the nature of some characters,“entity”),independently of the nickname or personality under which it appears in each comic book.In this way we have obtained6486nodes,appearing in12942comic books.3Analysis of the networkFrom the data contained in the MCP database,we have built up a bipartite graph(also known as mode2graph),with nodes corresponding to either Marvel characters or comicbooks,and edges from every character to all the books where it has appeared.We have extracted then from this bipartite graph the MU network,as its projection on its set of characters,and we have used PAJEK,a program for large network analysis[4],to compute most of the key values in our study of MU.In this section we discuss in some detail the results we have obtained,which are numerically summarized in Tables1and2.Table1Basic data on appearances of characters in comic books.Number of characters:6486Number of books:12942Mean books per character:14.9Mean characters per book:7.47Distribution of characters per book:P b(k)∼k−3.12Distribution of books per character:P c(k)∼k−0.6610−k/18953.1The bipartite graphThe bipartite graph summarizing the MCP database contains6486nodes corresponding to characters and12942nodes corresponding to comic books,and96662edges going from the characters to the books where they appear.A Marvel character appears typically in about14.9comic books.The number of appearances spans from1to1625:this greatest value corresponds to Spider-Man.The average number of characters per comic book is7.47with a range spanning from1to111:this last value is achieved by Issue1of Contest of Champions(1982),where the Grandmaster and the Unknown took every superhero in the planet and selected two teams to battle it out.We shall denote by P b(k)the distribution of ingoing edges,and by P c(k)the distribution of outgoing edges in this bipartite graph.That is,P b(k)represents the probability that a comic book has k characters appearing in it,and P c(k)represents the probability that a character appears in k comic books.To obtain the bestﬁt of these distributions we have logarithmically binned the data and performed a linear regression of log(P(r))on log(r). We have found that P b(k)follows the power-law tailP b(k)∼k−3.1228.The resulting histogram,together with the tail distribution is shown in Figure1.The dis-tribution of P b is similar to what can be found in real-life networks and is a new example of the ubiquity of Zipf’s law.On the other hand the bestﬁt for the distribution of P c(k)is diﬀerent of what is normally found in bipartite graphs associated to collaboration networks.The bestﬁtting distribution we found isP c(k)∼k−0.664410−k/1895.The exponent of only0.66is much smaller than other values published for similar networks, that usually ranges from2to3.Also,the presence of a cutoﬀhas been seldom reported1510501000.50.10.050.010.0050.001Number of characters per bookfrequencyofbooks Fig.1.Distribution of characters per comic books in the bipartite graph.The horizontal axis corresponds to the number of characters that appear in a comic book,while the vertical axis represent the frequency of books with those many characters.Note that the scales on both axis are logarithmic.The dashed line shows the tail probability distribution P b (k )∼k −3.12.in the literature.It is also of note that the ﬁtting is not only of the tail,but of all the histogram,with a high correlation of 0.992.The histogram together with the distribution found is shown in Figure 2.15105010050010000.50.10.050.010.0050.001Number of books per characterfrequencyofcharacters Fig.2.Distribution of books per character in the bipartite graph.The horizontal axis corresponds to the number of comic books in which a character appears,while the vertical axis represents the frequency of characters that appear in those many books.Note that the scales on both axis are logarithmic.The dashed line shows the probability distribution P c (k )∼k −0.6610−k/1895.These distributions will be the starting point to create a null random model against which to compare the characteristics of the Marvel Universe network.This model will be described in the next section.3.2The null random modelTo gain some perspective on the results obtained from the MU network,we compare them to a null random model.A reasonable random model would seem to be one with its same setof nodes and whose links have been generated by simply tossing a(possibly charged)coin: each link exists,independently of the other ones,with aﬁxed probability p.We shall call this a random network.Adjusting p,we can create a random network with as many nodes as our network and with expected number of links equal to the number of links in our network. This null model is quite popular and has been often times used.Recently,Newman et al.[16]have stated that given that collaboration networks are created from bipartite graphs,a better null random model from which our expectations about net-work structure should be measured is obtained by projecting random bipartite graphs with predetermined distributions of ingoing and outgoing edges.We have followed this approach in this paper.More speciﬁcally,the null random model MU-R we are going to compare the MU network to is obtained in the following way.We start from a random bipartite graph, which we shall call a MU-BR graph,with6486nodes-characters and12942nodes-books, and whose edges have been randomly created following exactly the same distributions P c(k) and P b(k)of outgoing and ingoing edges as those of the bipartite graph obtained from the MCP database in the previous subsection.Then,a MU-R graph is the projection of this random bipartite graph on its set of nodes-characters:i.e.,its nodes correspond to characters and its links represent to be connected to the same book in a MU-BR graph.The theoretical data corresponding to this random model have been computed through the formulas given by Newman et al.in loc.cit.3.3Basic dataOur MU network has N MU=6486nodes(characters)and M MU=168267links,i.e.pairs of characters that have collaborated in some comic book.We would like to mention that the actual number of collaborations is569770,but this value counts all collaborations in the Marvel Universe history,and while there are91040pairs of characters that have only met once,other pairs have met quite often:for instance,every pair of members of the Fantastic Four has jointly appeared in around700comic books(more speciﬁcally,this range of collaborations of the members of the Fantastic Four runs between668joint appearances of the Thing and the Invisible Woman to744joint appearances of the Thing and the Human Torch).The number of characters that have jointly appeared with a given character in some comic book is given by the degree of this character in the network.The average value for this degree in the MU collaboration network is2M MUgraph we would expect all569770collaborations to form diﬀerent links,which is about 3.4times the actual number of links in the MU network.As a consequence,the average degree in MU-R is the average degree in MU multiplied by this same factor,and would therefore become175.69:should the MU collaboration network(or,rather,the bipartite graph representing character appearances in books)have been created in a purely random way,a Marvel character would have collaborated on average with more than175other characters.It is shown[16,§V.A]that in the Hollywood graph and in several scientiﬁc collaboration networks the actual average degree is consistently smaller than the theoretical average degree of the corresponding random model,but not by such a large factor as the one found here. This indicates that Marvel characters are made to collaborate repeatedly with the same characters,which reduces their total number of co-partners well below the expected number in the random model,and that they collaborate quite more often with the same people than real movie actors or scientists do.This probably should be a hint of the artiﬁciality of the Marvel Universe.Table2Summary of results of the analysis of the MU network.Mean partners per character:51.88Size of giant component:6449characters(99.42%)Mean distance:2.63Maximum distance:5Clustering coeﬃcient:0.012Distribution of partners:P(k)∼k−0.7210−k/21673.4The giant componentTwo nodes in a network are said to be connected when there is at least one path in the network,made of consecutive links,that connects them.In a collaboration network,this means that two nodes are connected when they can be linked through a path of intermediate collaborators,or rather,in our case,co-partners.As mentioned before,ﬁnding such a path, and more speciﬁcally the shortest one,between any actor or actress and Kevin Bacon in the Hollywood network,is the goal of the Kevin Bacon game.In general,two nodes in a collaboration network need not be connected.But,in all large enough,sensible real-life networks,almost every node is connected to almost every other node.More speciﬁcally,large collaboration networks(and other large social networks)usually contain a very large subset of nodes—around80%to90%of all nodes—that are connected to each other:when this happens,this large subset of nodes with their corresponding links is called the giant component of the network.Also,Newman et al.[16]show that in random collaboration networks giant components do also occur,provided the corresponding random bipartite graphs have enough edges.MU contains a giant component of6449nodes,which cover99.42%of the characters in it.Let us also mention that the largest group of connected characters in the MU networkoutside this giant components has only9members.3.5SeparationThe distance between two connected nodes in a network is deﬁned as the length(the number of links)of the shortest path connecting them,i.e.,the least number of links we have to traverse in order to move from one node to the other within the network.Notice that the number of links in a path is equal to the number of intermediate nodes plus one,and thus we could also say that the distance between two connected nodes is the least number of intermediate nodes visited by a path connecting them plus one.For instance,the Kevin Bacon game asks for the distance of any actor or actress to Kevin Bacon in the Hollywood network,as the least number of intermediate co-partners plus one linking that actor or actress to Kevin Bacon.And it is popular among mathematicians to compute Erd¨o s numbers,that is his/her distance to P.Erd¨o s in the mathematicians’collaboration network.We have calculated all distances between all pairs of connected nodes in MU.The greatest distance between two connected nodes,called the diameter of MU in the usual network-theoretical terminology,is5.It implies that there is always a chain of at most4collaborators connecting any two connectable characters in the Marvel Universe.We have also computed the mean of all distances in the network,which provides the average separation of two characters in it.The value of this average separation is2.63.Thus,on average,any pair of characters in the MU network can be connected through a path of at most two consecutive partners.This is larger than the expected value in the MU-R network, which is1.45.Again,the reason is that only a third of the links in MU-R do appear in MU. Nevertheless,the values of both the diameter and the average separation in the MU network are signiﬁcantly smaller than the values of real-life networks reported so far.Finally,we have computed the center of the giant component,the character that minimizes the sum of the distances from it to all other nodes in the component.It turns out to be Captain America,who is,on average,at distance1.70to every other character.3.6ClusteringIn most social networks,two nodes that are linked to a third one have a higher probability to be linked between them:two acquaintances of a given person probably know each other. This eﬀect is measured using the clustering coeﬃcient,that is deﬁned as follows.Given a node v in a network,let k v be its degree,i.e.,the number of neighbors of v,and let N v be the number of links between these k v neighbors of v.If all these nodes were linked to each other,then N v would be equal to the number of unordered pairs of nodes belonging to this set of k v neighbors,i.e.,to k v(k v−1)/2.The clustering coeﬃcient C v of node v rates the diﬀerence between the actual value N v and this greatest value by taking their quotient2N vC v=Thus,this coeﬃcient C v measures the fraction of neighbors of node v that are linked.Notice that0≤C v≤1.The clustering coeﬃcient C of a network is then deﬁned as the mean value of the clustering coeﬃcients of all its nodes.It represents the probability that two neighbors of an arbitrary node are linked.All collaboration networks studied so far,and in general most social networks,have large clustering coeﬃcients.For instance,the clustering coeﬃcient of the Hollywood network is 0.199,1showing that two actors that have collaborated(possibly in diﬀerentﬁlms)with a third actor,have greater probability of being partners in a movie than two arbitrary,ran-domly chosen,actors.A similar eﬀect appears in scientiﬁc collaboration networks:except for MEDLINE,all other scientiﬁc collaboration networks studied so far have their cluster-ing coeﬃcients between,roughly,0.3and0.8,which tells us that a large fraction of the collaborators of a scientist collaborate with each other.This large clustering,together with a low value of the average distance between connected nodes,is taken as the deﬁnition of small-world networks[18].Actually,the word“large”means large compared to the expected value of the clustering coeﬃcient in a null random model.Depending on the choice of the null random model the results diﬀer.It is worthwhile to dedicate some time to discuss the diﬀerences as this will shed some light on the nature of the Marvel Universe and how it diﬀers from real-life collaboration networks.In a random network with n nodes and m links,it can be proved that the expected value of the clustering coeﬃcient is nothing but the probability p that two randomly selected nodes are connected;in other words,2mC random=1Thisﬁgure is its last value,published by Newman et al.[16],and is quite diﬀerent from the ﬁgure0.79previously published by Watts[18]when the network was quite smaller.Alamos e-Print Archive collaboration networks are between twice and2.3times the expected clustering coeﬃcient of the corresponding null random model.So,in this sense,the tendency to clustering in the Marvel Universe is similar to that of real-life collaboration networks. Our analysis shows that the Marvel Universe behaves“realistically”when compared to MU-R,but not when compared to a random network.Real-life collaboration networks have as clustering coeﬃcient roughly twice the one of their null random model,and the latter turns out to be highly clustered.The clustering coeﬃcient of the MU network is also roughly twice the one of its null random model,but this null random model is not highly clustered, having a clustering coeﬃcient only three times that of a random network with the same number of nodes and links.We believe that,as we already argued in connection with the average degree,this is a hint of the artiﬁciality of the bipartite graph which projects into MU.It seems that Marvel writers have not assigned characters to books in the same way as natural interactions would have done it,with the global eﬀect that the combination of the distributions P c(k)and P b(k)is very diﬀerent from what would be found in real-life networks,yielding non-clustered graphs.But,once we have these distributions,the Marvel Universe behaves realistically and is diﬀerent in a signiﬁcant way from a random network.3.7Distribution of the number of partnersAn interesting statistical datum that can be used to distinguish random networks from non-random networks is the distribution P(k)of degrees in the network.For every positive integer k,let P(k)denote the fraction of nodes in a given network that have degree k.In a random network with n nodes and m links,the expected value for P(k)follows a binomial distributionP(k)= n−1k p k(1−p)n−1−kwhere2mp=。