Community detection based on a semantic network

As information technology has advanced, people are turning more frequently to electronic media for communication, and social relationships are increasingly found in online channels. Massive amounts of the real data collected from online social networks (e.g., Internet newsgroups, BBS, and chat rooms) are network structured. Discovering the latent communities therein is a useful way to better understand the properties of a virtual social network. However, community-detection tasks were infeasible in previous studies of online social networks, especially with large-scale or weighted networks.In this paper, we constructed a semantic network using the semantic information extracted from comment content. In our modeling, we considered the impact of the weight on every edge and focused on the “giant component” of the online social network to reduce computational complexity; thus, our method can handle large-scale networks. In the experimental work, we evaluated our method using real datasets and compared our approach with several previous methods based on comment interactions; the results show that our method is much faster, more effective and robust.

► We construct a semantic network which attaches nodes and sides with computer-understandable semanteme. ► The small-world effect and the skewed degree distribution are both found in the semantic network. ► A stronger semantic correlation leads to a more obvious community structure. ► Community structure in the semantic network is more significant than in other comment interaction networks.