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CS-ComDet: A compressive sensing approach for inter-community detection in social networks

Mahyar, H ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1145/2808797.2808856
  3. Publisher: Association for Computing Machinery, Inc , 2015
  4. Abstract:
  5. One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and illustrate that the proposed method detects the inter-community links accurately even with low number of measurements (i.e. when the number of measurements is less than half of the number of existing links in the network)
  6. Keywords:
  7. Compressive sensing ; Inter-community detection ; Social networks ; Population dynamics ; Signal reconstruction ; Social networking (online) ; Social sciences ; Community detection ; Community structures ; Compressive sensing ; Network node ; Real-world networks ; Sparse signal recoveries ; Compressed sensing
  8. Source: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, 25 August 2015 through 28 August 2015 ; 2015 , Pages 89-96 ; 9781450338547 (ISBN)
  9. URL: http://dl.acm.org/citation.cfm?doid=2808797.2808856