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

Sanjay Kumar and B.S. Panda

Physica A: Statistical Mechanics and its Applications, 2020, vol. 553, issue C

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.

Keywords: Complex network; Influence maximization; Node centrality; SIR model; Social Network; VoteRank; WVoteRank (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:553:y:2020:i:c:s0378437120300479

DOI: 10.1016/j.physa.2020.124215

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