Overlapping Community Detection in Social Network Based on Microblog User Model

Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping communities in social networks, and its novelty lies in that it combines with the Microblog User Model (MUM) which can reflect the interest of the user accurately. First, the MUM network, which is an undirected and weighted network, is constructed by computing the similarity among MUMs. Afterwords, Overlapping Community Detection based on MUM (OCD-MUM) is performed to partition the network. A community stops expanding when the fitness function reaches a local maximum. The communities detected are locally optimized. A user's interest is not only decided by the MUM, but it is also affected by the communities the user belongs to. The community model can reflect the interest of the community. The MUM is updated with community model of its communities, and therefore the interest of the user can be predicted by these communities. Our experiment result shows that OCD-MUM has a higher modularity Q value than traditional methods and the predicted interest is more close to the real world situations.