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International Conference on Computational Social Networks

CSoNet 2015: Computational Social Networks pp 104–115Cite as

Improve Network Clustering via Diversified Ranking

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9197))

Abstract

Clustering is one fundamental task in network analysis. A widely-used clustering method is k-means clustering, where clustering is iteratively refined by minimizing the distance between each data point and its cluster center. For k-means clustering, one key issue is initialization, which heavily affects its accuracy and computational cost. This issue is particularly critical when applying k-means clustering to graph data where nodes are not embedded in a metric space. In this paper, we propose to use diversified ranking method to initialize k-means clustering, i.e., finding a set of seed nodes. In diversified ranking, seed nodes are figured out by considering their centrality and diversity in a unified manner. With seed nodes as starting points, k-means clustering is used to cluster nodes into groups. We apply the proposed method to detect communities in synthetic network and real-world network. Results indicate that the proposed method exhibits high effectiveness and efficiency.

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Author information

Authors and Affiliations

  1. CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Bing-Jie Sun, Hua-Wei Shen & Xue-Qi Cheng

Authors
  1. Bing-Jie Sun
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  2. Hua-Wei Shen
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  3. Xue-Qi Cheng
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Corresponding author

Correspondence to Hua-Wei Shen .

Editor information

Editors and Affiliations

  1. Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA

    My T. Thai

  2. Towsen University, Towson, Maryland, USA

    Nam P. Nguyen

  3. Chinese Academy of Sciences, Beijing, China

    Huawei Shen

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© 2015 Springer International Publishing Switzerland

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Sun, BJ., Shen, HW., Cheng, XQ. (2015). Improve Network Clustering via Diversified Ranking. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-21786-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21785-7

  • Online ISBN: 978-3-319-21786-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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