Community Detection in Bipartite Networks Using a Noisy Extremal Optimization Algorithm
The network community structure detection problem consists in finding groups of nodes that are more connected to each other than to the rest of the network. While many methods have been designed to deal with this problem for general networks, there are few methods that deal with bipartite ones. In this paper we explore the behavior of an optimization method designed for identifying the community structure of unweighted networks when dealing with bipartite networks. We find that by using the specific Barber modularity, results are comparable with those reported in literature.
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