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Consensus Clustering Approach for Discovering Overlapping Nodes in Social Networks

Published: 13 March 2016 Publication History

Abstract

Community discovery is an important problem that has been addressed in social networks through multiple perspectives. Most of these algorithms discover disjoint communities and yield widely varying results with regard to number of communities as well as community membership. We utilize this information positively by interpreting the results as opinions of different algorithms regarding membership of a node in a community. A novel approach to discovering overlapping nodes is proposed based on Consensus Clustering and we design two algorithms, namely core-consensus and periphery-consensus. The algorithms are implemented on LFR networks which are synthetic bench mark data sets created for community discovery and comparative performance is presented. It is shown that overlapping nodes are detected with a high Recall of above 96 % with an average F-measure of nearly 75% for dense networks and 65% for sparse networks which are on par with high-performing algorithms in the literature.

References

[1]
S. Arora, R. Ge, S. Sachdeva, and G. Schoenebeck. Finding overlapping communities in social networks: Toward a rigorous approach. In Proceedings of the 13th ACM Conference on Electronic Commerce, EC '12, pages 37--54, New York, NY, USA, 2012. ACM.
[2]
A. Goder and V. Filkov. Consensus clustering algorithms: Comparison and refinement. SIAM, 2008.
[3]
J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Morgan Kauffman, 2012.
[4]
A. Lancichinetti and S. Fortunato. Consensus clustering in complex networks. Scientific Reports, 2:1--7, 2012.
[5]
A. Lancichinetti, S. Fortunato, and J. Kertãl'sz. Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys, 11:2--17, 2009.
[6]
A. Lancichinetti, S. Fortunato, and F. Radicchi. Benchmark graphs for testing community detection algorithms. Physical review E, 78(4):046110, 2008.
[7]
H. Shen, X. Cheng, K. Cai, and M.-B. Hu. Detect overlapping and hierarchical community structure. Physica A, 388:1706, 2009.
[8]
J. J. Whang, D. F. Gleich, and I. S. Dhillon. Overlapping community detection using seed set expansion. In Proceedings of the 22nd ACM, pages 2099--2108. ACM, 2013.
[9]
J. Xie, S. Kelley, and B. Szymanski. Overlapping community detection in networks: the state of the art and comparative study. ACM Computing Surveys, 45, 2013.
[10]
J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. In Proceedings of ICDM, 2012.

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  1. Consensus Clustering Approach for Discovering Overlapping Nodes in Social Networks

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    CODS '16: Proceedings of the 3rd IKDD Conference on Data Science, 2016
    March 2016
    122 pages
    ISBN:9781450342179
    DOI:10.1145/2888451
    • General Chairs:
    • Madhav Marathe,
    • Mukesh Mohania,
    • Program Chairs:
    • Mausam,
    • Prateek Jain
    © 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 13 March 2016

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