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Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

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Abstract

Influence maximization is an important research problem in the field of network science because of its business value. It requires the strategic selection of seed nodes called “influential nodes,” such that information originating from these nodes can reach numerous nodes in the network. Many real-world networks, such as transportation, communication, and social networks, are weighted networks. Influence maximization in a weighted network is more challenging compared to that in an unweighted network. Many methods, such as weighted degree rank, weighted h-index, weighted betweenness, and weighted VoteRank techniques, have been used to order the nodes based on their spreading capabilities in weighted networks. The VoteRank method is a popular method for finding influential nodes in an unweighted network using the idea of a voting scheme. Recently, the WVoteRank method was proposed to find the seed nodes; it extends the idea of the VoteRank method by considering the edge weights. This method considers only 1-hop neighbors to calculate the voting score of every node. In this study, we propose an improved WVoteRank method based on an extended neighborhood concept, which takes the 1-hop neighbors as well as 2-hop neighbors into account for the voting process to decide influential nodes in a weighted network. We also extend our proposed approach to unweighted networks. We compare the performance of the proposed improved WVoteRank method against the popular centrality measures, weighted degree, weighted closeness, weighted betweenness, weighted h-index, and weighted VoteRank on several real-life and synthetic datasets of diverse sizes and properties. We utilize the widely used stochastic susceptible–infected–recovered information diffusion model to calculate the infection scale, the final infected scale as a function of time, and the average distance between spreaders. The simulation results reveal that the proposed method, improved WVoteRank, considerably outperforms the other methods described above, including the recent WVoteRank.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi, 110042, India

    Sanjay Kumar

  2. Department of Computer Science and Engineering, Bennett University, Greater Noida, UP, 201310, India

    Ankit Panda

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  1. Sanjay Kumar
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  2. Ankit Panda
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Correspondence to Sanjay Kumar.

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Kumar, S., Panda, A. Identifying influential nodes in weighted complex networks using an improved WVoteRank approach. Appl Intell 52, 1838–1852 (2022). https://doi.org/10.1007/s10489-021-02403-5

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