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Identifying Communities and Key Vertices by Reconstructing Networks from Samples

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  • Bowen Yan
  • Steve Gregory

Abstract

Sampling techniques such as Respondent-Driven Sampling (RDS) are widely used in epidemiology to sample “hidden” populations, such that properties of the network can be deduced from the sample. We consider how similar techniques can be designed that allow the discovery of the structure, especially the community structure, of networks. Our method involves collecting samples of a network by random walks and reconstructing the network by probabilistically coalescing vertices, using vertex attributes to determine the probabilities. Even though our method can only approximately reconstruct a part of the original network, it can recover its community structure relatively well. Moreover, it can find the key vertices which, when immunized, can effectively reduce the spread of an infection through the original network.

Suggested Citation

  • Bowen Yan & Steve Gregory, 2013. "Identifying Communities and Key Vertices by Reconstructing Networks from Samples," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    • Handle: RePEc:plo:pone00:0061006
    DOI: 10.1371/journal.pone.0061006
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    References listed on IDEAS

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    1. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    2. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
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