Interaction behavior enhanced community detection in online social networks

Community detection in online social networks (OSNs) has attracted a great deal of attention in recent years due to its potential prospects in applications such as recommendation systems, spam detection, targeted advertising, etc. However, community detection methods that have been proposed so far rely mostly on static information, such as the topology of the network, the attributes of the users, etc. In this paper, we propose a framework that enhances community detection with user interaction behaviors in OSNs since such behaviors should be important factors in characterizing the communities. In the framework, the various features of user interaction behaviors are expressed in the form of interaction indices through utilizing the cumulative distribution function and interaction divergence. The indices are then transformed into edge weights through a logistic regression model and combined with the static information to construct a weighted graph for the OSN. Finally, community detection is realized by applying a modularity maximization algorithm. A comprehensive evaluation using diverse real -world datasets shows that our proposed framework has superior performance over comparable ones in deriving high -quality communities. Critical metrics, such as modularity, average clustering coefficient, and average conductance, are evaluated to demonstrate the effectiveness of our approach. Furthermore, the synergy between network topology and user interaction behavior in community detection is also investigated.