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Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach

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  • Kumar, Sanjay
  • Panda, B.S.

Abstract

Efficient modeling of information diffusion in an online social network, like viral distribution of a market product or rumor control, can be achieved through the most influential nodes in the system. Hence, to pass the information to a maximum extent of the network or keep it confined to a lesser extent in the case of rumor, it is essential to find the influential nodes. Many classical centralities have been proposed in literature with certain limitations. Recently Vote Rank based method was introduced to find the seed nodes. It selects a set of spreaders based on a voting scheme where voting ability of each node is same and each node gets the vote from its neighbors. But we argue that the voting ability of each node should be different and should depend on its topological position in the network. In this paper, we propose a coreness based VoteRank method called NCVoteRank to find spreaders by taking the coreness value of neighbors into consideration for the voting. Experiments and simulations using Susceptible–Infected–Recovered (SIR) stochastic model on many real datasets show that our proposed method, NCVoteRank, outperforms some of the existing popular methods such as PageRank, K-shell, Extended Coreness, VoteRank, and WVoteRank.

Suggested Citation

  • Kumar, Sanjay & Panda, B.S., 2020. "Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    • Handle: RePEc:eee:phsmap:v:553:y:2020:i:c:s0378437120300479
    DOI: 10.1016/j.physa.2020.124215
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    References listed on IDEAS

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    Cited by:

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    5. Samir, Ahmed M. & Rady, Sherine & Gharib, Tarek F., 2021. "LKG: A fast scalable community-based approach for influence maximization problem in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

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