Network community detection is a task of dividing a set of network’s nodes into groups, such that intra-group
connections are more dense than inter-group connections. We consider a specific type of clustering, so-called multi-view
clustering, which deal with a set of graphs defined on the same set of nodes, but different edges. The goal is to divide
nodes into subgroups taking into account all graphs. We propose an ensemble method for multi-view clustering, applicable
to any greedy algorithm with nodes traversal. Instead of traversing nodes of the graphs individually, our approach does
a co-clustering of all networks’ nodes. Decision about node color takes into account connections in all input graphs. We
demonstrate the performance of our method, applied on a popular Louvain modularity algorithm, using real dataset and a
synthetic dataset (with known clustering structure).
community detection, multi-view clustering, ensemble methods, Louvain modularity
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