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RNA: A Reject Neighbors Algorithm for Influence Maximization in Complex Networks

Author

Listed:
  • Dongqi Wang

    (Software College of Northeastern University, Shenyang 110169, China)

  • Jiarui Yan

    (Software College of Northeastern University, Shenyang 110169, China)

  • Dongming Chen

    (Software College of Northeastern University, Shenyang 110169, China)

  • Bo Fang

    (Software College of Northeastern University, Shenyang 110169, China)

  • Xinyu Huang

    (Software College of Northeastern University, Shenyang 110169, China)

Abstract

The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.

Suggested Citation

  • Dongqi Wang & Jiarui Yan & Dongming Chen & Bo Fang & Xinyu Huang, 2020. "RNA: A Reject Neighbors Algorithm for Influence Maximization in Complex Networks," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1313-:d:395838
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