Improving the quality of overlapping community detection through link addition based on topic similarity

Community detection in social networks is usually done based on the density of connections between groups of nodes. However, these links do not necessarily represent an actual friendship especially in online social networks. There are users with declared friendship connections but without actual communication and no common interests. Most of the works in this area can be divided into two groups: topology-based and topic-based. The former usually leads to communities each containing diverse topics, and the latter leads to communities each with a consistent topic but with diverse structure. In this paper, we measure the similarity between users using topic models to generate virtual links for users with common interests. Moreover, in order to reduce the effect of useless links between users, we weight the network by measuring similarity of users' topics, so we could generate conforming communities, which contain only one topic or a group of consistent topics. The test results on Enron email dataset have shown the superior performance of our proposed method in the task of community detection.