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Improving Node Similarity for Discovering Community Structure in Complex Networks

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Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

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Abstract

Community detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community structures in complex networks can extract plenty of useful information, such as the structural features, network properties, and dynamic characteristics of the community. Several community detection methods introduced different similarity measures between nodes, and their performance can be improved. In this paper, we propose a community detection method based on an improvement of node similarities. Our method initializes a level for each node and assigns nodes into a community based on similarity between nodes. Then it selects core communities and expands those communities by layers. Finally, we merge communities and choose the best community in the network. The experimental results show that our method achieves state-of-the-art performance.

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Notes

  1. 1.

    http://www-personal.umich.edu/~mejn/netdata/.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Food Industry, Ho Chi Minh City, Vietnam

    Phuong N. H. Pham

  2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  3. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

Authors
  1. Phuong N. H. Pham
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  2. Hien T. Nguyen
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  3. Vaclav Snasel
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Corresponding author

Correspondence to Hien T. Nguyen .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

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Pham, P.N.H., Nguyen, H.T., Snasel, V. (2016). Improving Node Similarity for Discovering Community Structure in Complex Networks. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-42345-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

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