Community detection: Topological vs. topical

被引:87
作者
Ding, Ying [1 ]
机构
[1] Indiana Univ, Sch Lib & Informat Sci, Bloomington, IN 47405 USA
关键词
Community detection; Topics; Communities; Coauthor network; COCITATION ANALYSIS; BIBLIOMETRIC INFORMATION; AUTHOR COCITATION; SCIENCE; CITATION; TEXT;
D O I
10.1016/j.joi.2011.02.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The evolution of the Web has promoted a growing interest in social network analysis, such as community detection. Among many different community detection approaches, there are two kinds that we want to address: one considers the graph structure of the network (topology-based community detection approach); the other one takes the textual information of the network nodes into consideration (topic-based community detection approach). This paper conducted systematic analysis of applying a topology-based community detection approach and a topic-based community detection approach to the coauthorship networks of the information retrieval area and found that: (1) communities detected by the topology-based community detection approach tend to contain different topics within each community; and (2) communities detected by the topic-based community detection approach tend to contain topologically-diverse sub-communities within each community. The future community detection approaches should not only emphasize the relationship between communities and topics, but also consider the dynamic changes of communities and topics. Published by Elsevier Ltd.
引用
收藏
页码:498 / 514
页数:17
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