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Heterogeneous Network Community Detection Algorithm Based on Maximum Bipartite Clique

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

Abstract

The community mining of heterogeneous networks is a hot issue to study the big data of the network, and the original structure of the heterogeneous network and its information can fully exploit the community structure in the network. However, the existing algorithms mainly analysis one type of objects in the heterogeneous network, and the algorithms about the heterogeneous nodes which constitute the community structure is rarely studied. Therefore, this paper introduces the theory about maximum bipartite clique: Firstly, regarding the largest maximum bipartite clique that the key node belongs to as initial community. Then, the community is expanded based on the similarity between the neighbor node of the community and the initial community in quantitative. Finally, a reasonable community structure is mined and the simulation experiments are carried out on artificial networks and real heterogeneous networks. The experimental results show that the algorithm has relatively high community accuracy and modularity in community detection, which proves the rationality and validity of the algorithm.

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Acknowledgement

This article is supported by National Natural Science Foundation of China under grant No. 71461017.

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Authors and Affiliations

  1. School of Economics and Business Administration, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Xiaodong Qian

  2. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Lei Yang

  3. School of Institute of Electrical and Automation Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Jinhao Fang

Authors
  1. Xiaodong Qian
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  2. Lei Yang
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  3. Jinhao Fang
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Corresponding author

Correspondence to Xiaodong Qian .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Qian, X., Yang, L., Fang, J. (2018). Heterogeneous Network Community Detection Algorithm Based on Maximum Bipartite Clique. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_19

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2202-0

  • Online ISBN: 978-981-13-2203-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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