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Link-Prediction Enhanced Consensus Clustering for Complex Networks

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  • Matthew Burgess
  • Eytan Adar
  • Michael Cafarella

Abstract

Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that “consume” the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

Suggested Citation

  • Matthew Burgess & Eytan Adar & Michael Cafarella, 2016. "Link-Prediction Enhanced Consensus Clustering for Complex Networks," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0153384
    DOI: 10.1371/journal.pone.0153384
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    References listed on IDEAS

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    1. Atieh Mirshahvalad & Johan Lindholm & Mattias Derlén & Martin Rosvall, 2012. "Significant Communities in Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-7, March.
    2. Hailiang Huang & Bruno M Jedynak & Joel S Bader, 2007. "Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps," PLOS Computational Biology, Public Library of Science, vol. 3(11), pages 1-20, November.
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