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Combinatorial Structural Clustering (CSC): A Novel Structural Clustering Approach for Large Scale Networks

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

With the development of the brain cognition science and big data technologies, effective graph clustering is a key technique to uncover the brain mechanism, especially in resting state functional connectivity analysis. In this paper, a combinatorial structural clustering (CSC) algorithm is proposed for large scale networks. A structural similarity feature from adjacency structures of outliers and hubs is introduced to brain functional connectivity networks. Experimental results illustrate that our approach has some advantages compared with SCAN.

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Acknowledgments

This work is partly supported by the National Natural Science Foundation of China (Grant No. 61472058), and the Fundamental Research Funds for the Central Universities (Grant No. 3132016027).

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Liang Chen, Hongbo Liu, Weishi Zhang & Bo Zhang

Authors
  1. Liang Chen
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  2. Hongbo Liu
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  3. Weishi Zhang
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  4. Bo Zhang
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Corresponding author

Correspondence to Hongbo Liu .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Chen, L., Liu, H., Zhang, W., Zhang, B. (2017). Combinatorial Structural Clustering (CSC): A Novel Structural Clustering Approach for Large Scale Networks. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_42

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_42

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-53480-0

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