document: Community evolution in dynamic multi-mode networks A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur . Identifying communities in a multi-mode 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 the membership of groups often evolve gradually . In a dynamic multi-mode network , both actor membership and interactions can evolve , which poses a challenging problem of identifying community evolution . In this work , we try to address this issue by employing the temporal information to analyze a multi-mode network . A spectral framework and its scalability issue are carefully studied . Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships .
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