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Community Detection in Bipartite Networks: Algorithms and Case studies

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Complex Systems and Networks

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

There is increasing motivation to study bipartite complex networks as a separate category and, in particular, to investigate their community structure. We outline recent work in the area and focus on two high-performing algorithms for unipartite networks, the modularity-based Louvain and the flow-based Infomap. We survey modifications of modularity-based algorithms to adapt them to the bipartite case. As Infomap cannot be applied to bipartite networks for theoretical reasons, our solution is to work with the primary projected network. We apply both algorithms to four projected networks of increasing size and complexity. Our results support the conclusion that the clusters found by Infomap are meaningful and better represent ground truth in the bipartite network than those found by Louvain.

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Notes

  1. 1.

    Figure reprinted with permission from Ref. [28]. ©2014 by the American Physical Society.

  2. 2.

    Infomap available for download on the link: www.mapequation.org/.

  3. 3.

    Louvain available for download on the link: https://sites.google.com/site/findcommunities/.

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Acknowledgments

We are very grateful to Assoc. Prof. Murray Aitken for supplying us with the cleaned affiliation network data for the Noordin Top terrorist network; to Dr Tiago Peixoto for supplying us with the cleaned IMDB database; and to Assoc. Prof. Chris Bellman and Ms Sarah Taylor for assistance in using the ArcGIS mapping software on our clustered NSW crime network. The first author would like to thank the Ministry of Finance of Saudi Arabia for supporting his research. The work of the second author was partly supported by Department of Defence of Australia Agreement 4500743680. This work forms part of the PhD thesis of the first author, taken under the supervision of the second author.

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Authors and Affiliations

  1. School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, 3001, Australia

    Taher Alzahrani & K. J. Horadam

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  1. Taher Alzahrani
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  2. K. J. Horadam
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Correspondence to K. J. Horadam .

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Editors and Affiliations

  1. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Jinhu Lü

  2. School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia

    Xinghuo Yu

  3. Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China

    Guanrong Chen

  4. Department of Mathematics, Southeast University, Nanjing, China

    Wenwu Yu

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Alzahrani, T., Horadam, K.J. (2016). Community Detection in Bipartite Networks: Algorithms and Case studies. In: Lü, J., Yu, X., Chen, G., Yu, W. (eds) Complex Systems and Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47824-0_2

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  • DOI: https://doi.org/10.1007/978-3-662-47824-0_2

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