Extracting Influential Nodes in Social Networks Based On Community Structure

  • Firas Sabar Miften University of Thi-Qar, College of Education for Pure Science, Iraq
  • Zainab Naseem University of Thi-Qar, College of Education for Pure Science, Iraq
  • Evan Abdulkareem Huzam University of Thi-Qar, College of Education for Pure Science, Iraq
Keywords: social networks, community detection, influential nodes, influence propagation, influence maximization.

Abstract

Influence maximization (IM) is the process focuses on finding active users who make that maximizes the spread of influence into the network. In recent years, community detection has attracted intensive interest especially in the implementation of clustering algorithms in complex networks for community detection. In this paper the social network was divided into communities using the proposed algorithm which is called (CDBNN) algorithm, CDBNN stands for Community Detection Based on Nodes Neighbor. The key nodes (candidate nodes) were extracted using the degree centrality in each community. The propagates model (PSI) was used to information propagates through the network. Finally, using closeness centrality to extract the influential nodes from the network. Experimental results on the real network are efficient for influence propagates, compared with three known proposals.

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Published
2021-02-18
Section
Articles