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A high-performance algorithm for finding influential nodes in large-scale social networks

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Abstract

As one of the significant issues in social networks analysis, the influence maximization problem aims to fetch a minimal set of the most influential individuals in the network to maximize the number of influenced nodes under a diffusion model. Several approaches have been proposed to tackle this NP-hard problem. The traditional approaches failed to develop an efficient and effective solution due to the exponential growth of the size of social networks (due to massive computational overhead). In this paper, a three-stage framework based on the community detection approach is devised, namely LGFIM. In the first stage, the search space was controlled by partitioning the network into communities. Simultaneously, three heuristic methods were presented for modifying the community detection algorithm to extract the optimal communities: core nodes selection, capacity constraint on communities, and communities combination. These extracted communities were highly compatible with the information propagation mechanism. The next stages apply a scalable and robust algorithm at two different levels of the network: 1. Exploring the local scope of communities to select the most influential nodes of each community and construct the potential influential nodes set 2. Exploring the global scope of the network to select the target influential nodes among potential influential nodes set. Experimental results on various real datasets proved that LGFIM could achieve remarkable results compared with the state-of-the-art algorithms, especially acceptable influence spread, much better running time, and more applicable to massive social networks.

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Availability of data and materials

All of the mentioned datasets are available at the SNAP library on the Stanford University website https://snap.stanford.edu/data/ or  http://konect.cc/networks/ website.

Code availability

These codes are available at https://github.com/mohsentaherinia/LGFIM.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors would like to thank the anonymous reviewers for their valuable comments that helped to improve the quality of this manuscript.

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Authors and Affiliations

  1. Department of Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran

    Mohsen Taherinia & Mahdi Esmaeili

  2. School of Computer Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran

    Behrouz Minaei-Bidgoli

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  1. Mohsen Taherinia
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  2. Mahdi Esmaeili
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Correspondence to Mahdi Esmaeili.

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Taherinia, M., Esmaeili, M. & Minaei-Bidgoli, B. A high-performance algorithm for finding influential nodes in large-scale social networks. J Supercomput 78, 15905–15952 (2022). https://doi.org/10.1007/s11227-022-04418-2

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