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Influence maximization in community-structured social networks: a centrality-based approach

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Abstract

Influence maximization is a task in social network analysis that involves selecting a group of k individuals, known as the “seed set,” from the network to maximize the projected number of users influenced, termed as “influence spread.” The most important aspect of the influence maximization problem is identifying the most impactful communities and nodes within the network, rather than selecting notable nodes from the entire network. Indeed, unlike most existing approaches, influential node selection as a full network does not assure influence maximization in all clusters. This paper selects seed nodes with elevated betweenness centrality and eigencentrality within each community, communities are identified through the Girvan–Newman method, for influence propagation. The study employs the linear threshold and independent cascade models to assess the speed of influence propagation. Results suggest that choosing seed nodes from each community using these centrality measures is more effective than randomly selecting nodes from the entire network and also from the communities. Moreover, this study is beneficial even if nodes are selected using centrality analysis from the entire network. This approach can help to manage the spread of influence and improve influence maximization in community-structured social networks.

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Availability of data and materials

All the datasets are publicly available in the open-source platforms.

Notes

  1. https://docs.dgl.ai/en/2.0.x/generated/dgl.data.KarateClubDataset.html.

  2. https://networkx.org/documentation/stable/auto_examples/algorithms/plot_girvan_newman.html.

  3. https://www.researchgate.net/publication/305702413_Efficient_modularity_optimization_by_self-avoiding_walk/figures?lo=1.

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Authors and Affiliations

  1. Computer Science and Engineering, Future Institute of Engineering and Management, Sonarpur Station Road, Kolkata, West Bengal, 700150, India

    Maitreyee Ganguly

  2. Information Technology, Government College of Engineering and Ceramic Technology, 73, Abinash Chandra Banerjee Ln, Kolkata, West Bengal, 700010, India

    Paramita Dey

  3. Computer Science and Engineering, Jadavpur University, 188, Raja Subodh Chandra Mallick Rd, Kolkata, West Bengal, 700032, India

    Sarbani Roy

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  1. Maitreyee Ganguly
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  2. Paramita Dey
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Contributions

MG contributed to writing—the original draft, data curation, implementation and preparing the figures and table. PD and SR helped in conceptualization, methodology, formal analysis, and final editing. All authors reviewed the manuscript.

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Correspondence to Maitreyee Ganguly.

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Ganguly, M., Dey, P. & Roy, S. Influence maximization in community-structured social networks: a centrality-based approach. J Supercomput 80, 19898–19941 (2024). https://doi.org/10.1007/s11227-024-06217-3

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