文章基本信息

标题：Analysis of Spectral clustering approach for tracking community formation in social network
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作者：Sanjiv Sharma ; G.N. Purohit
期刊名称：International Journal of Social Networking and Virtual Communities
印刷版ISSN：2252-8784
出版年度：2012
卷号：1
期号：1
DOI：10.11591/socnetvircom.v1i1.780
语种：English
出版社：Institute of Advanced Engineering and Science
摘要：The study of tracking community formation in social networks is an active area of research. A common pattern among the cohesive subgroup of people in a network is considered as a community which is a partition of the entire network structure. In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra method and very often outperforms traditional clustering algorithms such as the k-means algorithm. Existing method of community tracking methods is based on hierarchical clustering algorithm. This paper establishes that spectral clustering is an efficient way for tracking community formation in social network.