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K-Hop Community Search Based on Local Distance Dynamics

  • Lijun Cai
  • Tao MengEmail author
  • Tingqin He
  • Lei Chen
  • Ziyun Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric, which has attracted a lot of attention in recent years. However, most of existing metric-based algorithms either tend to include the irrelevant subgraphs in the identified community or have computational bottleneck. Contrary to the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of k-hop and local distance dynamics model, which can natural capture a community that contains the query node. Extensive experiments on large real-world networks with ground-truth demonstrate the effectiveness and efficiency of our community search algorithm and has good performance compared to state-of-the-art algorithm.

Keywords

Community search Interaction model Complex network 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61174140, 61472127, 61272395); China Postdoctoral Science Foundation (2013M540628, 2014T70767); Natural Science Foundation of Hunan Province (14JJ3107); Excellent Youth Scholars Project of Hunan Province (15B087).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lijun Cai
    • 1
  • Tao Meng
    • 1
    Email author
  • Tingqin He
    • 1
  • Lei Chen
    • 1
  • Ziyun Deng
    • 2
  1. 1.College of Information Science and EngineeringHunan UniversityChangshaChina
  2. 2.Changsha Commerce and Tourism CollegeChangshaChina

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