Community detection: Topological vs. topical

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.

► 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.