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EDGly: detection of influential nodes using game theory

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Abstract

Identifying those nodes that play a critical role within a network is of great importance. Many applications such as gossip spreading, disease spreading, news dispersion, identifying prominent individuals in a social network, etc. may take advantage of this knowledge in a complex network. The basic concept is generally to identify the nodes with the highest criticality in a network. As a result, the centrality principle has been studied extensively and in great detail, focusing on creating a consistent and accurate location of nodes within a network in terms of their importance. Both single centrality measures and group centrality measures, although, have their certain drawbacks. Other solutions to this problem include the game-theoretic Shapley Value (SV) calculations measuring the effect of a collection of nodes in complex networks via dynamic network data propagation process. Our novel proposed algorithm aims to find the most significant communities in a graph with community structure and then employs the SV-based games to find the most influential node from each community. A Susceptible-Infected-Recovered (SIR) model has been employed to distinctly determine each powerful node's capacity to spread. The results of the SIR simulation have also been used to show the contrast between the spreading capacity of nodes found through our proposed algorithm and that of nodes found using SV-algorithm and centrality measures alone.

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Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Minni Jain, Aman Jaswani, Ankita Mehra & Laqshay Mudgal

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  1. Minni Jain
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  2. Aman Jaswani
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  3. Ankita Mehra
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  4. Laqshay Mudgal
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Correspondence to Minni Jain.

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Jain, M., Jaswani, A., Mehra, A. et al. EDGly: detection of influential nodes using game theory. Multimed Tools Appl 81, 1625–1647 (2022). https://doi.org/10.1007/s11042-021-11440-8

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  • DOI: https://doi.org/10.1007/s11042-021-11440-8

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