Abstract
Community detection plays a crucial role in many complex networks, including the increasingly important class of bipartite networks. Modularity-based community detection algorithms for bipartite networks are hampered by their well known resolution limit. Unfortunately, the high-performing random walk based algorithm Infomap, which does not have the same constraint, cannot be applied to bipartite networks.To overcome this we integrate the projection method for bipartite networks based on common neighbors similarity into Infomap, to acquire a weighted one mode network that can be clustered by the random walks technique. We also compare results obtained from this process with results in the literature. We illustrate the proposed method on four real bipartite networks, showing that the random walks technique is more effective than the modularity technique in finding communities from bipartite networks as well.
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Alzahrani, T., Horadam, K.J., Boztas, S. (2014). Community Detection in Bipartite Networks Using Random Walks. In: Contucci, P., Menezes, R., Omicini, A., Poncela-Casasnovas, J. (eds) Complex Networks V. Studies in Computational Intelligence, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-05401-8_15
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DOI: https://doi.org/10.1007/978-3-319-05401-8_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05400-1
Online ISBN: 978-3-319-05401-8
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