Community detection: Topological vs. topical

• Published in: Journal of informetrics 2011-10, Vol.5 (4), p.498-514
• Main Author: Ding, Ying
• Format: Article
• Language: English
• Subjects: Bibliometrics; Co authorship; Coauthor network; Communities; Community detection; Detection; Information retrieval; Research methods; Social networks; Topics
• ISSN: 1751-1577; 1875-5879
• DOI: 10.1016/j.joi.2011.02.006

► This paper applied the topology-based and the topic-based community detection approaches to the coauthorship networks of the information retrieval. ► The results are consistent with the hypothesis 1: Communities detected by the topology-based community detection approaches tend to contain topically-diverse sub-communities within each community. ► The results are consistent with the hypothesis 2: Communities detected by the topic-based community detection approaches tend to contain topologically-diverse sub-communities within each community. ► It proposes that community detection should consider both the topological and topical features of the networks. 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.