Identifying Evolving Groups in Dynamic Multimode Networks
A multimode network consists of heterogeneous types of actors with various interactions occurring between them. Identifying communities in a multimode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and its group structure often evolve unevenly. In a dynamic multimode network, both group membership and interactions can evolve, posing a challenging problem of identifying these evolving communities. In this work, we try to address this problem by employing the temporal information to analyze a multimode network. A temporally regularized framework and its convergence property are carefully studied. We show that the algorithm can be interpreted as an iterative latent semantic analysis process, which allows for extensions to handle networks with actor attributes and within-mode interactions. Experiments on both synthetic data and real-world networks demonstrate the efficacy of our approach and suggest its generality in capturing evolving groups in networks with heterogeneous entities and complex relationships.
multimode networks data mining community detection community evolution dynamic networks