Overlapping community detection in labeled graphs

In a recent paper with Esther Galbrun and Nikolaj Tatti, presented in the journal of Data Mining and Knowledge Discovery, we worked on the problem of discovering overlapping communities in networks with labeled vertices. The model is motivated by social networks, where vertex labels are used to represent information about individuals, such as occupation, hobbies, preferences, etc. The hypothesis is that the vertex labels can be used to derive and explain the community structure in the network. Continue reading