社会网络分析论文：社会网络分析动态性社区发现改进k-均值核心节点相似度增量分析

【关键词】社会网络分析动态性社区发现改进k-均值核心节点相似度增量分析

【英文关键词】Dynamic Social Network Analysis Dynamic Nature Community Detection

ModifiedK-means Core Nodes Similarity IncrementalAnalysis

社会网络分析论文：动态社会网络社区发现算法研究

【中文摘要】现实世界中，社会网络以朋友关系网络、科研人员合著关系网络、电力网络等形式广泛存在于多个领域。目前，社会网络分析已经成为数据挖掘中的一个研究热点。作为社会网络分析中的一项重要内容，社区发现吸引了大量来自多个领域的专家学者的密切关注并且已经产生了大量的研究成果。然而，作为社会网络的一个重要属性，动态性在大多数研究中没有被提及。因而，针对动态网络中的社区发现算法研究较少。动态社会网络中的社区发现通常是建立在某种静态算法基础上的。而现有的社区发现算法通常存在着需要额外的先验知识作为输入和社区划分质量不高等问题。针对这些问题，本文提出了一种基于改进的k均值算法思想的高质量社区发现算法——MKBCD。该算法包含了一种新的初始核心节点选择方法，并给出了相关阈值的通用选择方案和一个确定非核心节点社区归属的两阶段

策略。在Zachary karate club等真实和虚拟数据集上的实验证明了新算法的效率以及在挖掘高质量社区上的优势。在此基础上还提出了一种动态网络中骨干节点挖掘算法，能够从动态变化的社会网络中挖掘每个社区的骨干节点集合。增量式算法是一种在动态变化的网络中挖掘社区结构的算法，适用于网络结构变化频繁，但是短时间内不会发生较大变异而且变化不影响整体社区结构的场合。现有增量式算法存在着运行时间较长和算法精度较低等问题，无法实现效率和精度同时达到一个较高的水平。本文在现有工作的基础之上提出了一种基于增量分析的动态社区发现算法CDBIA，分析了增量对网络中相关节点的影响及其传递过程。在动态Zachary网络和虚拟数据集上的实验结果表明，CDBIA算法在保持较低的时间复杂度的同时，在社区结构的稳定性和社区划分质量上的都能得到较好的效果。

【英文摘要】Social networks are widespread in many areas of the real world such as friendshipnetworks, scientists’co-author relationship networks, and information networks and so on.Along with the rapid development of technology, Facebook, Twitter, blog community andother new forms of social networks have appeared in recent years. Nowadays, social networkanalysis has become an important mission and hot issue in data mining.As an important property of social networks, community structure and relative researchhave attracted

considerable concern of more and more experts and scholars from a lot ofdifferent areas such as sociology, bioinformatics and computer science. By now, there existmany community detection methods represented by GN, K-L and Rachicci. Some of themeven have been applied in actual practice. However, most present algorithms are staticmethods, meaning that they only analysis a single static network graph, while the dynamicnature, networks may change over time, is rarely considered. In fact, dynamic social networkanalysis has just started since2003. So it has wide research space and applications.Before this paper, the author read a lot of articles about social network analysis,clustering, classification and other data mining technology and learned the graph theory,theory of probability and mathematical statistics and other related knowledge. Based on theabove work, we do a lot of research work about detecting communities in dynamic socialnetwork and its evolution analysis.This paper first introduced the research status of social network analysis and summarizedthe advantages and shortcomings of existing community detection methods. Based on relativeresults and in order to put forward the corresponding solutions to solve problems in thesealgorithms, this paper