CiteSpace图文教程
《citespace教程》课件
数据准备
数据收集
根据研究需求，收集相关领域的文献数据，如通过数据库检索、网络爬虫等途 径。
数据清洗
对收集到的数据进行预处理，如去除重复、格式转换等，确保数据质量。
参数设置
时间范围设置
根据研究主题，选择合适的时间范围 ，以便对特定时间段内的数据进行可 视化分析。
阈值设置
根据数据量大小和可视化效果要求， 合理设置阈值，控制节点的数量和网 络密度。
02
开发历程
详细介绍Citespace软件的创始人及其研发团队，包括他们的教育背 景、专业领域以及在Citespace开发过程中的贡献。
概述Citespace软件的起源、发展历程以及在各个阶段所取得的成果 和突破。
软件功能
01
可视化分析
详细介绍Citespace软件的可视 化分析功能，包括对网络、聚
主题演化路径
Citespace可以绘制主题演化路径图，帮助 研究者理解和跟踪研究主题的发展脉络。
04
Citespace应用案例
案例一：研究热点分析
总结词
通过Citespace分析，揭示研究领域的热 点话题和主要研究领域。
VS
详细描述
利用Citespace软件对大量文献数据进行 可视化分析，通过关键词共现、突发性检 测等技术手段，可以清晰地展示某一领域 的研究热点和发展趋势。
软件兼容性问题
确保操作系统与Citespace版本兼容 ，必要时可尝试使用虚拟机或双系 统。
数据处理问题
01
数据导入问题
确保数据格式正确，并按照 Citespace要求的文件格式进行
导入。
02
数据处理速度慢
尝试优化数据处理参数，如降 低时间跨度、调整时间切片等
citespace使用指导PPT
AU Galea, S Ahern, J Resnick, H Kilpatrick, D Bucuvalas, M Gold, J Vlahov, D TI Psychological sequelae of the September 11 terrorist attacks in New York City. SO NEW ENGLAND JOURNAL OF MEDICINE LA English DT Article ID POSTTRAUMATIC-STRESS-DISORDER; NATIONAL COMORBIDITY SURVEY; MAJOR DEPRESSION; NATURAL DISASTER; SOCIAL SUPPORT; OKLAHOMACITY; PREVALENCE; PSYCHOPATHOLOGY; SURVIVORS; SYMPTOMS AB Background: The scope of the terrorist attacks of September 11, 2001, was unprecedented in the United States. We assessed the prevalence and correlates of acute posttraumatic stress disorder (PTSD) and depression among residents of Manhattan five to eight weeks after the attacks. Methods: We used random-digit dialing to contact a representative sample of adults living south of 110th Street in Manhattan. Participants were asked about demographic characteristics, exposure to the events of September 11, and psychological symptoms after the attacks. Results: Among 1008 adults interviewed, 7.5 percent reported symptoms consistent with a diagnosis of current PTSD related to the attacks, and 9.7 percent reported symptoms consistent with current depression (with ``current`` defined as occurring within the previous 30 days). Among respondents who lived south of Canal Street (i.e., near the World Trade Center), the prevalence of PTSD was 20.0 percent. ………… C1 New York Acad Med, Ctr Urban Epidemiol Studies, New York, NY 10029 USA. Columbia Univ, Mailman Sch Publ Hlth, Dept Epidemiol, New York, NY USA. Med Univ S Carolina, Natl Crime Victims Res & Treatment Ctr, Charleston, SC 29425 USA. Schulman Ronca & Bucuvalas, New York, NY USA. Bellevue Hosp Ctr, New York, NY 10016 USA. RP Galea, S, New York Acad Med, Ctr Urban EpidemiolStudies, Rm 556,1216 5th Ave, New York, NY 10029 USA. CR 2001, NY TIMES 1226, B2 *AM PSYCH ASS, 1994, DIAGN STAT MAN MENT *DEP HLTH HUMAN SE, 1999, MENT HLTH REP SURG G *US BUR CENS, 2000, STF3A DEP COMM BUR C
第1讲 CiteSpace及科学知识图谱
第1讲CiteSpace与科学知识图谱李杰1,2，陈超美31.上海海事大学海洋科学与工程学院2.上海海事大学科技情报研究所3. Drexel University-College of Computing andInformaticsChen C. Information visualization: Beyond the horizon[M]. Springer Science& Business Media, 2006.配套教程: 李杰, 陈超美著.CiteSpace科技文本挖掘及可视化[M].首都经济贸易大学出版社.2016.作者博客: 李杰博客：/u/jerrycueb；陈超美博客：/u/ChaomeiChen本讲基本内容CiteSpace简介及原理科学知识图谱导览CiteSpace应用现状及问题CiteSpace学习流程及其相关资料软件开发者陈超美，男，1960年9月生于北京。
美国德雷塞尔大学计算机与情报学学院教授，曾先后担任英国布鲁内尔大学客座教授和大连理工大学长江学者讲座教授。
研究方向为信息可视化、科学前沿图谱和科学发现理论。
发表科技论文200余篇，被引超过10000次。
出版著作科学计量学及数据可视化方面的著作近10部，并有多部被翻译成中文。
中文博客：/u/ChaomeiChen学术主页：/~cc345/Why CiteSpace？Google Metrics Array近12%的引用贡献来源于Citespace的一篇典型文献（1167/10005）。
如果加上其他与CiteSpace相关的应用被引，可能会达到30%-50%以上。
https:///citations?user=IjN4HSRsdakC&hl=enCiteSpace简介•陈超美（Chao-mei Chen）教授是美国德雷赛尔大学计算机与情报学教授，从2008年开始担任大连理工大学长江学者讲座教授，同时也是Drexel-DLUT 知识可视化与科学发现联合研究所（美方）所长。
《citespace教程》课件
热度分析
CiteSpace可以分析文献的引用和被引用情况， 生成热度图，帮助用户把握学术交流的热度和 趋势。
时间分析与演化分析
1
时间分析
CiteSpace可以对文献的时间序列进行分析，揭示文献在时间上的演化趋势和变 化规律。
2
演化分析
CiteSpace可以根据文献之间的引用和被引用关系，分析文献的演化过程，可视 化文献之间的关联和变化。
可定制性高
CiteSpace具有很高的可定 制性，用户可以根据自己的 研究需要自定义分析参数， 以获取最优的分析结果。
CiteSpace的功能特点
可视化分析
CiteSpace可以进行各种维度的 可视化分析，帮助用户深入理解 文献网络，挖掘出隐藏的知识和 规律。
时间序列分析
CiteSpace可以对文献的时间序 列进行分析，揭示文献在时间上 的演化趋势和变化规律。
2 产业界
可以帮助企业分析市场研 究文献，了解产品和市场 的变化趋势，为企业提供 决策支持
3 政府机构
可以帮助政府机构了解某 一政策或议题领域的热点 和趋势，为政策的制定和 实施提供参考
CiteSpace的安装与界面介绍
安装方式
CiteSpace可以免费下载安装， 安装包可以在其官网上下载。
主界面
《CiteSpace教程》PPT课 件
CiteSpace是一款功能强大的文献可视化工具，本课程将简要介绍CiteSpace 的特点、应用领域以及使用方法。
什么是CiteSpace？
文献分析工具
CiteSpace是一款文献分析 工具，其主要功能是进行科 学文献的分析和可视化。
基于引文网络
CiteSpace基于引文网络分 析方法，利用科学文献之间 的引文关系建立网络，并通 过对这个网络的可视化来实 现对文献信息的分析。
citespace使用及案例应用(PPT文档)
Web of Scienc CSSCI(Chinese Social Science Citation
Index)
Pubmed NSF Derwent Scopus arxiv e-Print CNKI SDSS(Sloan Digital Sky Survey)
RESNICK HS, 1993, J CONSULT CLIN
PSYCH, V61, P984 ROTHBAUM BO, 1992, J TRAUMA
STRESS, V5, P455
journal co-
C citation
CiteSpace 使用——系统使用/导入数据 1
/~cchen/citespace/
CiteSpace用的共被引记录信息
co-occurring burst terms
AU Galea, S Ahern, J Kilpatrick, D
Bucuvalas, M
A co-authorship
TI Psychological sequelae of the September 11
SO NEW ENGLAND JOURNAL OF MEDICINE
勾选聚类词的类型
勾选节点类型
对“引文”“共被 引”数进行调谐
对网络进行了最小生成树、 合并、年代切片处理
选择静态聚类、合并网视图
应用案例分析步骤——图谱判读
应用案例分析步骤——前沿、热点/趋势分析与报告
展开视图中各聚类 组节点文献研读
经过“pathfinder剪切视图”和“时区图”分 析及对其高引文献的分析整理，得到 六维力传感器近年研究方向的重大转移， 热点领域的重点分布，核心技术的主要构成， 新发展态势、趋向、领域、理论及技术等分 析结论以及综述报告
citespace使用指导PPT
AU Galea, S Ahern, J Resnick, H Kilpatrick, D Bucuvalas, M Gold, J Vlahov, D TI Psychological sequelae of the September 11 terrorist attacks in New York City. SO NEW ENGLAND JOURNAL OF MEDICINE LA English DT Article ID POSTTRAUMATIC-STRESS-DISORDER; NATIONAL COMORBIDITY SURVEY; MAJOR DEPRESSION; NATURAL DISASTER; SOCIAL SUPPORT; OKLAHOMACITY; PREVALENCE; PSYCHOPATHOLOGY; SURVIVORS; SYMPTOMS AB Background: The scope of the terrorist attacks of September 11, 2001, was unprecedented in the United States. We assessed the prevalence and correlates of acute posttraumatic stress disorder (PTSD) and depression among residents of Manhattan five to eight weeks after the attacks. Methods: We used random-digit dialing to contact a representative sample of adults living south of 110th Street in Manhattan. Participants were asked about demographic characteristics, exposure to the events of September 11, and psychological symptoms after the attacks. Results: Among 1008 adults interviewed, 7.5 percent reported symptoms consistent with a diagnosis of current PTSD related to the attacks, and 9.7 percent reported symptoms consistent with current depression (with ``current`` defined as occurring within the previous 30 days). Among respondents who lived south of Canal Street (i.e., near the World Trade Center), the prevalence of PTSD was 20.0 percent. ………… C1 New York Acad Med, Ctr Urban Epidemiol Studies, New York, NY 10029 USA. Columbia Univ, Mailman Sch Publ Hlth, Dept Epidemiol, New York, NY USA. Med Univ S Carolina, Natl Crime Victims Res & Treatment Ctr, Charleston, SC 29425 USA. Schulman Ronca & Bucuvalas, New York, NY USA. Bellevue Hosp Ctr, New York, NY 10016 USA. RP Galea, S, New York Acad Med, Ctr Urban EpidemiolStudies, Rm 556,1216 5th Ave, New York, NY 10029 USA. CR 2001, NY TIMES 1226, B2 *AM PSYCH ASS, 1994, DIAGN STAT MAN MENT *DEP HLTH HUMAN SE, 1999, MENT HLTH REP SURG G *US BUR CENS, 2000, STF3A DEP COMM BUR C
citespace知识图谱分析及操作
MAZURE CM, 2000, AM J PSYCHIAT,
V157, P896 NORTH CS, 1999, JAMA-J AM MED
ASSOC, V282, P755
C document
co-citation
RESNICK H, 1999, J ANXIETY DISORD, V13, P359
CiteSpace用的共被引记录信息
co-occurring burst terms
AU Galea, S Ahern, J Kilpatrick, D
Bucuvalas, M
A co-authorship
TI Psychological sequelae of the September 11
SO NEW ENGLAND JOURNAL OF MEDICINE
—Wetherell等
科学知识图谱基本理论
• 科学知识图谱知识背景 • 科学知识图谱基本方法 • 科学知识图谱作用
科学知识图谱应用—引文分析
引文分析 理论与方
法
1、说明科学知 识和情报内容的 继承和利用 2、标志科学的 发展
科学知识图谱应用—共被引分析
共被引分 析理论与
方法
1、从分析被引文献类 型、语种入手，可研究 科学文献体系的特征结 构及分布、利用等规律 2、从分析被引文献网 络及其变化，可研究学 科间关系、联系特征、 发展变化现状、发展趋 势
科学知识图谱基本理论
• 科学知识图谱知识背景 • 科学知识图谱基本方法 • 科学知识图谱作用
科学知识图谱基本方法
引文分析理论与方法
Citation Analysis
CiteSpace使用手册
CiteSpace使用手册CiteSpace使用手册1：安装与配置1.1 系统要求1.2 与安装1.3 配置步骤2：界面与菜单2.1 主界面2.2 导航菜单2.3 工具栏2.4 设置选项3：导入数据3.1 文件格式要求3.2 导入步骤3.3 数据预处理4：可视化分析4.1 知识图谱4.2 时间轴图4.3 关键词共现图 4.4 簇分析4.5 导出结果5：数据过滤与排序5.1 关键词过滤5.2 文献类型过滤 5.3 时间范围过滤 5.4 排序功能6：检索与搜索6.1 文献检索6.2 高级搜索6.3 检索结果导出7：图表操作7.1 缩放与平移7.2 节点与边的操作7.3 颜色与标签设置8：高级功能8.1 社会网络分析8.2 文献演化路径分析8.3 排他性分析8.4 自定义分析9：常见问题解答9.1 安装与配置问题9.2 数据导入问题9.3 可视化分析问题9.4 其他常见问题附件：本文档涉及附件，请参见附件部分。
法律名词及注释：1： CiteSpace：一款用于科学文献可视化分析的软件工具。
2：可视化分析：通过图形化的方式呈现数据，以便于观察、分析和发现数据中的模式、趋势和关联。
3：数据预处理：在数据分析之前对原始数据进行清洗、转换和归一化等处理，以达到更好的分析效果。
4：关键词共现图：展示关键词之间的共现关系，以便于分析研究领域内的热点和关联性。
5：簇分析：将文献根据某些相似性指标进行聚类，从而发现相关研究领域的研究集合。
6：社会网络分析：通过分析研究者之间的合作关系，揭示研究者、团队和机构之间的科学合作网络。
7：文献演化路径分析：分析文献之间的引用关系，揭示研究领域中的演化过程和研究方向的变化。
第3讲 CiteSpace安装及分析功能
第3讲CiteSpace 安装及分析功能李杰1,2，陈超美31.上海海事大学海洋科学与工程学院2.上海海事大学科技情报研究所3. Drexel University-College of Computing andInformatics配套教程: 李杰, 陈超美著.CiteSpace科技文本挖掘及可视化[M].首都经济贸易大学出版社.2016.作者博客: 李杰博客：/u/jerrycueb；陈超美博客：/u/ChaomeiChen本讲基本内容CiteSpace基本术语CiteSpace下载和安装界面介绍（功能参数区和可视化界面）CiteSpace数据分析的关键步骤CiteSpace结果解读的提示基本术语：CiteSpaceCiteSpace：引文空间是一款着眼于分析科学分析中蕴含的潜在知识，是在科学计量学、数据可视化背景下逐渐发展起来的一款引文可视化分析软件。
由于是通过可视化的手段来呈现科学知识的结构、规律和分布情况，因此也将通过此类方法分析得到的可视化图形称为“科学知识图谱”。
BSE和CJD研究领域的演变（引文空间的变化）/blog-496649-482376.html动画下载地址/~cchen/talks/demo/BSE_CJD_1981-2001_transp.exe基本术语：中介中心性Betweenness centrality：中介中心性是测度节点在网络中重要性的一个指标（此外还有度中心性、接近中心性等）。
CiteSpace中使用此指标来发现和衡量文献的重要性，并用紫色圈对该类文献（或作者、期刊以及机构等）进行重点进行标注。
出现紫圈的节点的中介中心性>=0.1基本术语：突发性探测Burst 检测：突发主题（或文献、作者以及期刊引证信息等）。
在CiteSpace中使用Kleinberg, J（2002）年提出的算法进行检测。
基本术语：引文年轮Citation tree-rings ：引文年环–代表着某篇文章的引文历史。
CiteSpace操作指南
The CiteSpace ManualVersion 0.96Chaomei ChenCollege of Computing and InformaticsDrexel UniversityHow to cite:Chen, Chaomei (2014) The CiteSpace Manual. /~cchen/citespace/CiteSpaceManual.pdfContents1How can I find the latest version of the CiteSpace Manual? (5)2What can I use CiteSpace for? (5)2.1What if I have Questions (7)2.2How should I cite CiteSpace? (7)2.3Where are the Users of CiteSpace? (8)3Requirements to Run CiteSpace (10)3.1Java Runtime (JRE) (10)3.2How do I check whether Java is on my computer? (10)3.3Do I have a 32-bit or 64-bit Computer? (12)4How to Install and Configure CiteSpace (12)4.1Where Can I download CiteSpace from the Web? (12)4.2What is the maximum number of records that I can handle with CiteSpace? (13)4.3How to configure the memory allocation for CiteSpace? (13)4.4How to uninstall CiteSpace (14)4.5On Mac or Unix-based Systems (15)5Get Started with CiteSpace (19)5.1Try it with a demonstrative dataset (19)5.1.1The Demo Project (20)5.1.2Clustering (23)5.1.3Generate Cluster Labels (25)5.1.4Where are the major areas of research based on the input dataset? (27)5.1.5How are these major areas connected? (28)5.1.6Where are the most active areas? (28)5.1.7What is each major area about? Which/where are the key papers for a given area?365.1.8Timeline View (38)5.2Try it with a dataset of your own (39)5.2.1Collecting Data (39)5.2.2Working with a CiteSpace Project (43)5.2.3Data Sources in Chinese (44)5.2.4How to handle search results containing irrelevant topics (45)6Configure a CiteSpace Run (47)6.1Time Slicing (47)6.3Configure the Networks (48)6.3.1Bibliographic Coupling (49)6.4Node Selection Criteria (49)6.4.1Do I have the right network? (50)6.5Pruning, or Link Reduction (50)6.6Visualization (51)7Interacting with CiteSpace (51)7.1How to Show or Hide Link Strengths (51)7.2Adding a Persistent Label to a Node (52)7.3Using Aliases to Merge Nodes (53)7.4How to Exclude a Node from the Network (55)7.5How to Use the Fisheye View Slider (55)7.6How to Configure When to Calculate Centrality Scores Automatically (56)7.7How to Save the Visualization as a PNG File (57)8Additional Functions (58)8.1Menu: Data (58)8.1.1CiteSpace Built-in Database (58)8.1.2Utility Functions for the Web of Science Format (61)8.1.3PubMed (62)8.2Menu: Network (64)8.2.1Batch Export to Pajek .net Files (64)8.3Menu: Geographical (64)8.3.1Generate Google Earth Maps (64)8.4Menu: Overlay Maps (67)8.4.1Add an Overlay (68)8.4.2Further Reading and Terms of Use (70)8.5Menu: Text (70)8.5.1Concept Trees and Predicate Trees (70)8.5.2List Terms by Clumping Properties (73)8.5.3Latent Semantic Analysis (74)9Selected Examples (75)10Metrics and Indicators (77)10.1Information Theoretic (77)10.2Structural (77)10.2.1Betweenness Centrality (77)10.2.2Modularity (77)10.2.3Silhouette (77)10.3Temporal (77)10.3.1Burstness (77)10.4Combined (77)10.4.1Sigma (77)10.5Cluster Labeling (78)10.5.1Term Frequency by Inversed Document Frequency (78)10.5.2Log-Likelihood Ratio (78)10.5.3Mutual Information (78)11References (78)1How can I find the latest version of the CiteSpace Manual?The latest version of the CiteSpace Manual is always at the following location:/~cchen/citespace/CiteSpaceManual.pdfYou can also access the manual from CiteSpace: Help ►View the CiteSpace Manual (PDF). It will open up the PDF file in a new browser window.Figure 1. The latest version of the CiteSpace Manual is accessible from CiteSpace itself.2What can I use CiteSpace for?CiteSpace is designed to answer questions about a knowledge domain, which is a broadly defined concept that covers a scientific field, a research area, or a scientific discipline. A knowledge domain is typically represented by a set of bibliographic records of relevant publications. It is your responsibility to prepare the most appropriate and representative dataset that contains adequate information to answer your questions.CiteSpace is designed to make it easy for you to answer questions about the structure and dynamics of a knowledge domain. Here are some typical questions:•What are the major areas of research based on the input dataset?•How are these major areas connected, i.e. through which specific articles?•Where are the most active areas?•What is each major area about? Which/where are the key papers for a given area?•Are there critical transitions in the history of the development of the field? Where are the ‘turning points’?The design of CiteSpace is inspired by Thomas Kuhn’s structure of scientific revolutions. The central idea is that centers of research focus change over time, sometime incrementally and other times drastically. The development of science can be traced by studying their footprints revealed by scholarly publications.Members of the contemporary scientific community make their contributions. Their contributions form a dynamic and self-organizing system of knowledge. The system contains consensus, disputes, uncertainties, hypotheses, mysteries, unsolved problems, and unanswered questions. It is not enough to study a single school of thought. In fact, a better understanding of a specific topic often relies on an understanding of how it is related to other topics.The foundation of the CiteSpace is network analysis and visualization. Through network modeling and visualization, you can explore the intellectual landscape of a knowledge domain and discern what questions researchers have been trying to answer and what methods and tools they have developed to reach their goals.This is not a simple task. Rather it is often conceptually demanding and complex. If you are about to write a novel, the word processor or a text editor can make the task easier, but it cannot help you to create the plot or enrich the character of your hero. Similarly, and probably to a greater extent, CiteSpace can generate X-ray photos of a knowledge domain, but to interpret what these X-ray photos mean, you need to have some knowledge of various elements involved. The role of CiteSpace is to shift some of the traditionally labor-some burdens to computer algorithms and interactive visualizations so that you can concentrate on what human users are most good at in problem solving and truth finding. However, it is probably easier to generate some mysterious looking visualizations with CiteSpace than to fully understand what these visualizations tell you and who may benefit from such findings.Figure 2. Hierarchically organized functions of CiteSpace, for example, GUI ►Pruning ►Pathfinder: true.2.1What if I have QuestionsIf you have a question regarding the use of CiteSpace, you should first check the manual whether your question is answered in the manual. You can do a simple search through the PDF file to find out.If the manual does not get you anywhere, you can ask your questions on the Facebook page of CiteSpace:https:///pages/CiteSpace/276625072366558You can also post questions to my blog on sciencenet:/home.php?mod=space&uid=496649Please refrain from sending me emails because you will have a much better chance to get my response from either the Facebook or the sciencenet blog.Generally speaking, thoughtful questions get answered quickly. Questions that you may be able to figure out the answer for yourself if you think a little bit more about it would have a lower priority in the answering queue; it is quite possible that some of them never get answered.2.2How should I cite CiteSpace?The following three publications represent the core ideas of CiteSpace.The 2004 PNAS paper is the initial publication on CiteSpace (Chen 2004). In hindsight, it could have been named CiteSpace I. The 19-page 2006 JASIST paper gives the most thorough and in-depth description of CiteSpace II’s key functions (C. M. Chen, 2006), plus a follow-up study of domain experts identified in the visualizations. The 2010 JASIST paper is even longer with 24 pages (C. Chen, Ibekwe-SanJuan, & Hou, 2010), which is the third of the trilogy. It describes technical details on how cluster labels are selected and how each of the three selection algorithms in comparison with labels chosen by domain experts.ReferenceCitations(Google Scholar)800 Chen, C. (2006). "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature." Journal of the AmericanSociety for Information Science and Technology 57(3): 359-377.394 Chen , C. (2004). "Searching for intellectual turning points: Progressive Knowledge Domain Visualization." Proc. Natl. Acad. Sci. USA101(Suppl.): 5303-5310.157 Chen, C., et al. (2010). "The structure and dynamics of co-citation clusters:A multiple-perspective co-citation analysis." Journal of the AmericanSociety for Information Science and Technology 61(7): 1386-1409.The most recent case study of a topic outside the realm of information science and scientometrics is a scienometric study of regenerative medicine (C. Chen, Hu, Liu, & Tseng, 2012).Chen, C., et al. (2012). "Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace."Expert Opinions on Biological Therapy 12(5): 593-608.2.3Where are the Users of CiteSpace?In terms of the cities where CiteSpace were used, China, the United States, and Europe are prominent. Brazil, Turkey, and Spain also have many cities on the chart.Figure 3. Cities with users of CiteSpace between August 2013 and March 2014 are shown on the map. The colors of markers depict the level of user intensity: green (1-10), yellow (10-100), red (100-1000), and the large red water dropshaped marker (1000+).Figure 4. The use of CiteSpace in China (August 2013 – March 2014).Figure 5. The use of CiteSpace in the United States (August 2013 – March 2014).Figure 6. The use of CiteSpace in Europe (August 2013 – March 2014).3Requirements to Run CiteSpace3.1Java Runtime (JRE)CiteSpace is written in Java. It is a Java application. You should be able to run it on a computer that supports Java, including Windows or Mac.CiteSpace is currently optimized for Windows 64-bit Java 7 (i.e. Java 1.7).To run a Java application on your computer, you need to have Java Runtime (JRE) installed on your computer.3.2How do I check whether Java is on my computer?Figure 7. Select Control Panel.Figure 8. Click into the Programs category to find the Java control panel.Figure 9. Locate the Java control panel.Figure 10. Java Control Panel. Choose the Java tab and press the View button to see more detail.Figure 11. Java Runtime 1.7 is installed.3.3Do I have a 32-bit or 64-bit Computer?You need to find out whether your computer has a 32-bit or a 64-bit operating system.Go to Control Panel ►System and Security ►System. You will see various details about your computer. Under the System type, you will see whether you have a 32-bit or a 64-bit operating system.Follow the link below for further instructions on how to install Java:/en/download/help/index_installing.xmlOnce you have Java Runtime setup on your computer, you can proceed to install CiteSpace.4How to Install and Configure CiteSpaceCiteSpace is provided as a zip file for 64-bit and 32-bit computers. For Mac users, you need to download the 64-bit version.4.1Where Can I download CiteSpace from the Web?You can download the latest version of CiteSpace from the following website:/~cchen/citespace/download.htmlFigure 12.The download page of CiteSpace.After you download the zip file to your computer, unpack the zip file to a folder of your choice.Figure 13. CiteSpace is unpacked to the D drive on a computer.Now you can start CiteSpace by double clicking on the StartCiteSpace file.If you need to modify the amount memory allocated for CiteSpace (more precisely for Java Virtual Machine on which CiteSpace to be running), you can edit StartCiteSpace as a plain text file with any text editor.4.2What is the maximum number of records that I can handle with CiteSpace?This question needs to be answered at two levels: the number of records processed by CiteSpace and the number of nodes visualized, i.e. you can see and interact with them in CiteSpace.The first number is the total number of records in your downloaded dataset. CiteSpace reads through each record in your download files.The second number is determined by the selection criteria you specify and by the amount of memory, i.e. RAM, available on your computer. The more RAM you can make available for CiteSpace, the larger sized network you can visualize with a faster response rate.The speed of processing is also affected by a few computationally expensive algorithms such as Pathfinder network scaling and cluster labeling. Empirically, the best options for Pathfinder network scaling would be 50~500 nodes per slice. With faster computers or if you can wait for a bit longer, you can raise the number accordingly.The completion time of cluster labeling is related to the size of your dataset. If the entire timespan of your dataset is 100 years but you will only need to consider the most recent 10 years, it will be a good idea to carve out a much smaller dataset as long as it covers the 10 years of interest. It will reduce the processing time considerably.4.3How to configure the memory allocation for CiteSpace?The performance of CiteSpace is influenced by the amount of memory accessible to the Java Virtual Machine (JVM) on which CiteSpace is running. To analyze a large amount of records, you should consider allocating as much as memory for CiteSpace to use.You can modify the StartCiteSpace.cmd file to optimize the setting. More specifically, modify line 14 in the file. For example, -Xmx2g means that CiteSpace may get a maximum of 2GB of RAM to work with. Save the file after making any changes. And restart CiteSpace.Figure 14. Configure the memory for Java in line 14.4.4How to uninstall CiteSpaceYou can use the following steps to remove cached copies of CiteSpace from your computer.Figure 15. In a Command Prompt window, type javaws –viewer.When you see a list of cached copies of CiteSpace in the Java Cache Viewer, select the items that you want to remove and then click on the button with a red cross.Figure 16. Select a cached copy of CiteSpace and remove the item.4.5On Mac or Unix-based SystemsThe following example shows you the basic steps to get started with CiteSpace on a Mac. First, go to the CiteSpace homepage in a browser such as Chrome and download the latest 64-bit version.Figure 17. On a Mac, go to the CiteSpace home page in a browser such as Chrome and download the latest 64-bit version. Once the download is completed, follow the option “Show in Finder.” It will take you to a list of files downloaded to your Mac. The most recent file should be the zip file for CiteSpace.Figure 18. Choose “Show in Finder.”Figure 19. The downloaded zip file is shown in your Finder.Double-click on the zip file to unzip the file to a folder in the current folder.Figure 20. The zip file is unzipped to a new folder on the list.Figure 21. The new folder contains CiteSpaceII.jar and a lib folder.The simplest way to get started with CiteSpace is to open the CiteSpaceII.jar by clicking on it while holding the “Control” key on Mac. Select Open from the pop-up menu.Figure 22. Click on the CiteSpaceII.jar while holding the “Control” key and select “Open.”Due to the Java security settings, you will see a dialog box with two options for Open or Cancel.Choose Open to proceed. It will not harm your computer.Figure 23. Choose “Open” from the dialog box to proceed.After you choose Open, CiteSpace is getting started on Mac. You will see its opening page asfollows. Choose “Agree” to continue.Figure 24. CiteSpace is now started on Mac.Figure 25. Screenshots of running the Demo project of CiteSpace on Mac.It is a good idea to get familiar with the basic functions of CiteSpace by going through the Demo project on terrorism, which is included in the zip file.If you want to configure various Java Virtual Machine parameters in more detail than what is shown in the above example, you may generate a bash file for your Mac as follows.The Mac equivalent of the StartCiteSpace.cmd would be a bash file, which should have a file extension of .sh and should be executable. Let’s name the file as StartCiteSpace.sh to be consistent.1.The content of the StartCiteSpace.sh file should have the following two lines:#!/bin/bashjava -Xms1g -Xmx4g -Xss5m -jar CiteSpaceIII.jar2.The following instruction turns the StartCiteSpace.sh file to an executable file:chmod +x StartCiteSpace.sh3.To invoke the executable file, simply type its name or double click on it.StartCiteSpace5Get Started with CiteSpace5.1Try it with a demonstrative datasetWhen you installed CiteSpace for the first time, a demonstrative dataset on terrorism research is setup for you to play with and get familiar with the major analytic functions in CiteSpace.If you have never used CiteSpace before, I strongly recommend you to start with this demo dataset.To launch CiteSpace, double click on the StartCiteSpace.cmd file. You will see a command prompt window first. This window will also display various information on the status and any errors.Figure 26. The command prompt window.You will see another window of “About CiteSpace” – it displays system information of your computer, including the Java version.To proceed, you need to click on the Agree button. CiteSpace may collect user driven events for research purposes.Figure 27. The “About CiteSpace” window. To proceed, click on the Agree button.Next, you will see the main user interface of CiteSpace.The user interface is divided into left and right halves. The left-hand side contains controls of projects (i.e. input datasets) and progress report windows. The right-hand side contains several panels for configuring the process with various parameters.In a nutshell, the process in CiteSpace takes an input dataset specified in the current project, constructs network models of bibliographic entities, and visualizes the networks for interactive exploration for trends and patterns identified from the dataset.The demo project contains a dataset on publications about terrorism research. These bibliographic records were retrieved from the Web of Science. See later sections on tips for how to construct your own dataset.5.1.1The Demo ProjectWe will start the process and explain how CiteSpace is designed to help you answer some of the key questions about a knowledge domain, i.e. a field of study, a research area, or a set of publications defined by the user.Press the green GO! button to start the process.Figure 28. The main user interface of CiteSpace.CiteSpace will read the data files in the current project (Demo) and report its progress in the two windows on the left-hand side of the user interface. When the modeling process is completed, you have three options to choose: Visualize, Save As GraphML, or Cancel.Visualize:This option will take you to the visualization window for further interactive exploration. Save As GraphML:This option will save the constructed network in a file in a common graph format. No visualization.Cancel:This option will not generate any interactive visualization nor save any files. It allowsyou to reconfigure the process and re-run the process.Figure 29. CiteSpace is ready to visualize the constructed network.If you click on the Visualize button, a new window will pop up. This is the Visualization Window. Initially you will see some movements on your screen with a black background. Once the movements are settled, the background color turns to white.Let’s focus on what the initial visualization tells us and then explore what else we can find by using additional functions.First, CiteSpace visualizes a merged network based on several networks corresponding to snapshots of consecutive years. In the Demo project example, the overall time span is from 1996 through 2003. The merged network characterizes the development of the field over time, showing the most important footprints of the related research activities. Each dot represents a node in the network. In the Demo case, the nodes are cited references. CiteSpace can generate networks of other types of entities. Here let’s focus on cited references only for now. Lines that connect nodes are co-citation links; again, CiteSpace can generate networks of other types of links. The colors of these lines are designed to show when a connection was made for the first time. Note that this is influenced by the scope and the depth of the given dataset.The color encoding makes it easy for us to tell which part of the network is old and which is new. If you see that some references are shown with labels, then you will know that these references are highly cited, suggesting that they are probably landmark papers in the field. A list on the left side of the window shows all the nodes appeared in the visualization. The list can be sorted by the frequency of citations, Betweenness centrality, or by year or references as text. You can alsochoose to show or hide a node on the list.Figure 30. The Visualization window.A control panel is shown on the right-hand side of the Visualization Window. You can change how node labels are displayed by a combination of a few threshold values through sliders. You can also change the size of a node by sliding the node size slider.To answer the typical questions we asked before, let’s use several functions in CiteSpace to obtain more specific information through clustering, labeling, and exploring.5.1.2ClusteringAlthough we can probably eyeball the visualized network and identify some prominent groupings, CiteSpace provides more precise ways to identify groupings, or clusters, using theclustering function.To start the clustering function, simply click on this icon .How do I know whether the clustering process is completed? You will see #clusters on the upper right corner of the canvas. In the Demo example, a total of 37 clusters of co-cited references are identified. Each cluster corresponds to an underlying theme, a topic, or a line of research.The signature of the network is shown on the upper left corner of the display. In particular, the modularity Q and the mean silhouette scores are two important metrics that tell us about the overall structural properties of the network. For example, the modularity Q of 0.7141 is relatively high, which means that the network is reasonably divided into loosely coupled clusters. The mean silhouette score of 0.5904 suggests that the homogeneity of these clusters on averageis not very high, but not very low either.Figure 32. The clustering process is completed. 37 clusters are identified (#clusters shown in the upper right corner).Modularity and silhouette scores are shown in the signature of the network on the left.Figure 33. Members of different clusters are shown in different colors.You can inspect various measures of each cluster in a summary table of all the clusters using: Clusters ►4. Summarization of Clusters. The Silhouette column shows the homogeneity of a cluster. The higher the silhouette score, the more consistent of the cluster members are, provided the clusters in comparison have similar sizes. If the cluster size is small, then a high homogeneity does not mean much. For example, cluster #9 has 7 members and a silhouette of 1.00, this is most likely due to the possibility that all 7 references are the citation references of the same underlying author. In other words, cluster #9 may reflect the citing behavior or preferences of a single paper, thus it is less representative.The average year of publication of a cluster indicates whether it is formed by generally recent papers or old papers. This is a simple and useful indicator.Figure 34. A summary table of clusters.5.1.3Generate Cluster LabelsTo characterize the nature of an identified cluster, CiteSpace can extract noun phrases from the titles (T in the following icon), keyword lists (K), or abstracts (A) of articles that cited the particular cluster.Let’s ask CiteSpace to choose noun phrases from titles (i.e. select the T icon). This process may take a while as CiteSpace needs to compute several selection metrics. Once the process is finished, the chosen labels will be displayed. By default, labels based on one of the three selection algorithms will be shown, namely, tf*idf. Our study has found that LLR usually gives the best result in terms of the uniqueness and coverage.Figure 35. Icons for performing Clustering and Labeling functions.Cluster labels are displayed once the process is completed. The clusters are numbered in the descending order of the cluster size, starting from the largest cluster #0, the second largest #1, and so on.Figure 36. Cluster labels are generated and displayed.To make it easier to see which clusters are the largest, you can choose to change the font size of the labels from the uniformed to proportional:Display ►Labe l Font Size ►Cluster: Uniformed/ProportionalThis is a toggle function. That means there are two states. Your selection will switch back and forth between the two states, i.e. either using a uniformed font size or proportional.Figure 37. Set the cluster labels’ font size proportional to their size.Figure 38. Cluster labels’ font sizes are proportional to the size of a cluster. The largest cluster is #0 on biologicalterrorism.5.1.4Where are the major areas of research based on the input dataset?This is one of the primary questions that CiteSpace is designed to answer. To answer this question, we will focus on the big picture of the collection of publications represented by your dataset. Let’s make a few adjustments with the sliders in the control panel on the right so that the information of our interest will be shown clearly and information that is less relevant to this question right now will be temporarily hidden from the view.1.Node SizeAt this level, we don’t really need to see the size of a node, although it provides rich information about the history of a node. Use the slider under Article Labeling ►Node Size ►[Slide to 0] (marked by the pointer #1 in the following figure).2.Cluster Label SizeThe font size of the cluster labels are controlled by a slider with two controls: one control the threshold for showing or hiding a label based on the size of the cluster (i.e. to make sure large-enough clusters are always labeled), and the other control the font size of the cluster labels (marked by the pointer #2 in the screenshot).3.Transparency of LinksDetailed links would be useful later, but we can ignore them for now using the transparency slider to set all the links’ transparency to the lowest level, i.e. invisible. In hindsight, a more accurate term would be completely transparent.After making these minor adjustments, it will be straightforward to answer the question: Where are the major areas of research? Evidently, the largest area (cluster #0 with the largest number of member references) is biological terrorism. The second largest is posttraumatic stress (cluster #1), i.e. PTSD. The third one is ocular injury (cluster #2). The fourth one is blast (cluster #3). And there are a few smaller clusters. So now we have a general idea what constituted terrorism research during the period of 1996 and 2003. You can repeat the process on a current dataset to get an up-to-date big picture.。
citespace使用指导PPT精品名师资料
0. Glossary
Betweenness centrality – a metric of a node in a network that measures how likely an arbitrary shortest path in the network will go through the node. Burst terms – single or multi-word phrases extracted from the title, abstract, or other fields of a bibliographic record and the frequency of the term bursts, i.e. sharply increases, over a period of time. Citation – an instance that a publication references to another publication. Citation half-life – the number of years that a publication receives half of its citations since its publication. Citation tree-rings – outwards growing rings of a node to depict its time series of citations. The thickness of a ring is proportional to the citations in the corresponding year. Cluster view – a network is visualized in a modified spring-embedder node placement algorithm. Co-authors – authors who appear in the author field of the same bibliographic record. Co-citation – an instance in which two items, such as authors, documents, or journals, that are cited by a publication. Color map – a spectrum of colors used by CiteSpace to depict temporal order of observations. EM clustering – Expectation Maximization (EM) clustering nodes based on various attributes such as citations, citation half-life, and betweenness centrality. The use of temporal attributes can help the visualization of emerging trends. MeSH terms – Medical Subject Heading terms are a set of controlled vocabulary compiled by the National Library of Medicine. CiteSpace shows MeSH terms assigned to nodes if there are matches in PubMed. Pathfinder network scaling – a network scaling algorithm that removes links that violate triangle inequality conditions so as to simplify a network by retaining salient links and paths only. Pivotal points – see Turning points. Publication types – study design types extracted from PubMed for clinical trial studies, including meta-analysis and randomized clinical trials. Spotlight – visualized networks rendered by fading out links that are not connecting pivotal points. Thresholds – selection criteria used by CiteSpace – items must have measures above threshold values to be included in modeling and visualization processes. Time slicing – a divide-and-conquer strategy that divides a period of time into a series of smaller windows. Time-zone view – a restricted view in which the movement of nodes is limited to vertical time zones corresponding to the time of their publication. Turning points – nodes of high betweenness centralities (> 1.00). Such nodes tend to be critical in network transitions from one time slice to another.
Citespace下载、出图入门教程(图文版)
一．简单介绍二．下载与安装三．知网示例四．术语解释五．常见问题•下载——官方下载网址：/~cchen/citespace/download/•开发者陈超美科学网博客（有软件最新相关内容）/home.php?mod=space&uid=496649•作用——辅助分析的工具，帮助我们找出学术文献中文字的关系(包括:作者,杂志,关键词,被引用词汇等等),并可视化表示出来。
但不能作为独立的分析结果，需要该领域的专家对其进行解释和分析。
•用途——帮助刚进入某领域研究的学者建立全面的认识；有利于分析学科的发展脉络；能够识别学科研究热点；帮助预测学科未来的发展走向。
•软件下载软件下载按钮Java运行环境下载按钮◆注意下载与电脑配置相匹配的版本（64位&32位）。
◆注意CiteSpace与Java下载对应版本。
•软件安装安装包解压后，点击该按钮，开始运行•_windows&_mac分别对应windows和mac系统电脑1.等待几秒之后，出现该界面；2.随后在光标处输入数字“2”，3.等待软件启动。
点击“不”点击“同意”1.数据导入与格式转换2.创建新项目3.设置时间分隔与阈值4.聚类分析与调整5.结果解读•操作流程注意：1.知网一次最多导出500条文献，最好按时间或者内容主题等分组导出。
2.将下载txt文件重命名为download开头，例：download_民族教育3.新建四个文件夹：input\output\data\project4.初始下载的文件存入input文件夹，格式转换后存入output文件夹，再将output文件夹中数据复制到data文件夹备用。
数据格式转换注意：分别对应选择刚刚建立的四个文件夹，导出文件存入output文件夹创建新项目项目名称项目保存目录数据所在目录保存开始停止聚类以标题给类命名以引文关键词命名以摘要命名命名算法节点大小的依据配色出现频次中心性最早出现年份•点击以上任意数据，可复制粘贴导出•可剔除不相关项关键词类标签节点标签连线标签每个节点为一个关键词。
citespace使用(2012讲课版)
新兴研究前沿析出方法
“NSF水污染”研究前沿分析网
利用突现词频（burst term）+共引（现）词频（ category、term、keyword或cited reference，从DCA混合分析网络揭示新兴前沿
“红外量子点-阱”新兴前沿分析网
数据设置
2.1.2 研究(学科)转折（关键）点分析
分析结论：“红外器
件”3名高发文作者，分 别为美国、加拿大等
2.2.4 科研竞争力 -- 研究水平分析
需求分析 对X所X引进人才“InN纳米花结构”研究水平评估 数据处理
1. 数据制备 数据设置
用SCI、ISTP、EI数据库，采用XX检索式，在主题项中检 到“2000-2010 InN纳米花结构”XX条文献数据 2. 导入CiteSpace；数据处理、构建分析网： 节点类型：phrases、cited reference 分析网类型：ACA + 学科聚类
分析结论：
美国
日本
“红外器件”研究 （发文）大国为： 美国、中国、日 本、德国、法国 等
“红外器件”国家+术语网
分析结论：“红外器件”方面，
中国主攻领域：量子阱红外 探测器等方面；美国、英国、 澳大利亚等分别涉及2个前沿 领域：红外光电探测器、毫 微米红外探测器
2.2.2 科研竞争力 — 研究机构分析
主要内容
科技知识查获路径
知识图谱及CiteSpace基本概念
CiteSpace应用案例分析
CiteSpace使用方法
1、知识图谱应用原理
概要
• 社会网络法 • 科学知识图情 • CiteSpace应用领域 • CiteSpace原理&概念
1.1 社会网络法基本概念
citespace使用指导 ppt课件
Citation – an instance that a publication references to another publication.
Citation half-life – the number of years that a publication receives half of its citations since its publication.
Citation tree-rings – outwards growing rings of a node to depict its time series of citations. The thickness of a ring is proportional to the citations in the corresponding year.
0. Glossary
Betweenness centrality – a metric of a node in a network that measures how likely an arbitrary shortest path in the network will go through the node.
科研文献的可视化分析(Citespace)
科技文献的可视化 分析
生物及医学学科馆员
科研工作的基础文献信息素养
Google Wave Mendeley zotero
mindmanager
信息 素养
了解图书馆资源 检索基本知识 常用数据库 RSS订阅
RefViz Quosa Citespace Publish or Perish
提取研究前沿术语
❖ 软件提供了词频增长检测 (burstdetection)算法，该算法 主要通过考察词频的时间分布，将那些频次变化率高、频次 增长速度快的“突发词”(bstterm)从大量题录的常用词中 检测出来，用词频的变动趋势，而不仅仅是词频的高低，来 分析科学的前沿领域和发展趋势。
❖ “突现”词可以展现知识领域的研究前沿和发展趋势。通过 生成共引文献网络以及施引文献主题词的共词网络，即得到 一个由这两个网络共同构成的共引与共词混合网 络 (hybridnetwork ofcitedartieleandeitingterms)图谱， 可以展示出学科知识领域的重要被引文献以及由施引文献主 题词所表达的重要研究领域或其前沿趋势。
Data Visualization
Scientific Visualization
Information Visualization
Information Visualization
2010’s mapping knowledge domains
Knowledge Visualization
1、 科学知识图谱（mapping knowledge domains ）
阀值选择
显示
1、以文本形式保存
可视检测
验证关键点
确定主题词和专业术语 收集数据 提取研究前沿术语 时区分割
citespace使用方法.ppt
使用步骤演示
——以国内红色旅游研究为例
1. 登录中国知网 2. 检索“关键词”或“篇名”中包含“红色旅游”字段文献 3. 得到结果如下：
4、选择并导出文献
2、选中 文献
3、导出参考文献
1、切换到50 篇每页
5、导出文献
1、全选文献
6、筛选文献（删除领导讲话、致辞、目录卷次、征稿启事等等无关文献）
2、导出参考文献
5、导出文献
1、选中Refworks格 式
2、导出
5、保存数据
注意：把保存 的数据改成以 “download_ XX”开头
6、数据转换
6、数据转换
1、选择“CNKI类型”
2、选择数据存放文件夹
3、点击转换
7、数据转换结果
转换前
转换后
8、建立分析项目
9、使用CiteSpace进行分析
超美(Chaomei Chen) 博士于2004年开发，2007年首次被 引入到国内 ➢ 两部分组成 1.Java JRE（运行环境） 2.CiteSpace软件包
➢ 官方网站
/~cchen/citespace/downloa d/
CiteSpace软件可视界面
CiteSpace软件在地理学、旅游学中的应用
中山大学周 素红老师首 先引入
CiteSpace软件的使用步骤
1. 确定关键词和专业术语 2. 收集数据 3. 提取研究前沿术语 4. 时区分割 5. 阈值选择 6. 显示 7. 可视化分析
使用步骤演示
——以国内红色旅游研究为例
开始之前： 1、首先在电脑D盘（或其它）建立空白文件夹，命名为 “红色旅游” 2、进入文件夹，再建立4个小文件夹，分别命名为 input、output、data、project