MCD: A Modified Community Diversity Approach for Detecting Influential Nodes in Social Networks

TL;DR

This paper introduces MCD, a novel method combining community detection and a modified diversity measure to identify influential nodes in social networks more accurately, outperforming existing methods across multiple datasets.

Contribution

The paper proposes a new community diversity approach extended to two-hop scenarios for better influence estimation in social networks.

Findings

MCD outperforms state-of-the-art methods on eight datasets.

MCD effectively reduces overlap in influence scope.

Experimental results confirm improved influence detection accuracy.

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.