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Journal of Intelligent Information Systems

Erschienen in:

26.01.2023

MCD: A modified community diversity approach for detecting influential nodes in social networks

verfasst von: Aaryan Gupta, Inder Khatri, Arjun Choudhry, Sanjay Kumar

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2023

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Abstract

Over the last couple of decades, Social Networks have connected people on the web from across the globe and have become a crucial part of our daily life. These networks have also rapidly grown as platforms for propagating products, ideas, and opinions to target a wider audience. This calls for the need to find influential nodes in a network for a variety of reasons, including the curb of misinformation being spread across the networks, advertising products efficiently, finding prominent protein structures in biological networks, etc. In this paper, we propose Modified Community Diversity (MCD), a novel method for finding influential nodes in a network by exploiting community detection and a modified community diversity approach. We extend the concept of community diversity to a two-hop scenario. This helps us evaluate a node’s possible influence over a network more accurately and also avoids the selection of seed nodes with an overlapping scope of influence. Experimental results verify that MCD outperforms various other state-of-the-art approaches on eight datasets cumulatively across three performance metrics.

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Titel: MCD: A modified community diversity approach for detecting influential nodes in social networks
verfasst von: Aaryan Gupta
Inder Khatri
Arjun Choudhry
Sanjay Kumar
Publikationsdatum: 26.01.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2023
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-023-00776-2

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