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Finding influential communities in massive networks

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Abstract

Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core to capture the influence of a community. Based on this community model, we propose a linear time online search algorithm to find the top-r k-influential communities in a network. To further speed up the influential community search algorithm, we devise a linear space data structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the data structure when the network is frequently updated. Additionally, we propose a novel I/O-efficient algorithm to find the top-r k-influential communities in a disk-resident graph under the assumption of \({{\mathcal {U}}}=O(n)\), where \({{\mathcal {U}}}\) and n denote the size of the main memory and the number of nodes, respectively. Finally, we conduct extensive experiments on six real-world massive networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

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Notes

  1. If the maximal k-core of G has more than one MCCs, the \(\mathsf {ICPS}\) is a forest, where each MCC generates a tree.

  2. The original core maintenance algorithms independently proposed in [19, 23] mainly focus on edge deletion and insertion.

  3. Suppose that each answer only contains the set of nodes in each community; otherwise, we simply compute the induced subgraph by the nodes in the answer.

  4. http://snap.stanford.edu.

  5. http://konect.uni-koblenz.de/networks.

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Acknowledgements

The work was supported in part by (i) NSFC Grants (61402292, U1301252), NSF-Shenzhen Grants (JCYJ20150324140036826, JCYJ20140418095735561), and Startup Grant of Shenzhen Kongque Program (827/000065); (ii) ARC DE140100999 and ARC DP160101513; (iii) Research Grants Council of the Hong Kong SAR, China, 14209314 and 14221716; (iv) China 863 Grants: 2015AA015305.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rong-Hua Li & Rui Mao

  2. Centre for QCIS, FEIT, University of Technology, Sydney, Australia

    Lu Qin

  3. The Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

Authors
  1. Rong-Hua Li
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  2. Lu Qin
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  3. Jeffrey Xu Yu
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  4. Rui Mao
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Correspondence to Jeffrey Xu Yu or Rui Mao.

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Li, RH., Qin, L., Yu, J.X. et al. Finding influential communities in massive networks. The VLDB Journal 26, 751–776 (2017). https://doi.org/10.1007/s00778-017-0467-4

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