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Detection of top-K central nodes in social networks: A compressive sensing approach

Mahyar, H ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1145/2808797.2808811
  3. Publisher: Association for Computing Machinery, Inc , 2015
  4. Abstract:
  5. In analysing the structural organization of a social network, identifying important nodes has been a fundamental problem. The concept of network centrality deals with the assessment of the relative importance of a particular node within the network. Most of the traditional network centrality definitions have a high computational cost and require full knowledge of network topological structure. On the one hand, in many applications we are only interested in detecting the top-k central nodes of the network with the largest values considering a specific centrality metric. On the other hand, it is not feasible to efficiently identify central nodes in a large real-world social network via calculation of centrality values for all nodes. As a result, recent years have witnessed increased attention toward the challenging problem of detecting top k central nodes in social networks with high accuracy and without full knowledge of network topology. To this end, we in this paper present a compressive sensing approach, called CS-TopCent, to efficiently identify such central nodes as a sparsity specification of social networks. Extensive simulation results demonstrate that our method would converge to an accurate solution for a wide range of social networks
  6. Keywords:
  7. Detection of central nodes ; Social networks ; Top k list of nodes ; Compressed sensing ; Signal reconstruction ; Social networking (online) ; Topology ; Central nodes ; Compressive sensing ; Computational costs ; Extensive simulations ; Network centralities ; Network topological structure ; Structural organization ; Palmprint recognition
  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 902-909 ; 9781450338547 (ISBN)
  9. URL: http://dl.acm.org/citation.cfm?doid=2808797.2808811