CKS: A Community-based K-shell Decomposition Approach using Community Bridge Nodes for Influence Maximization

TL;DR

This paper introduces CKS, a community-based K-shell decomposition method that leverages community structures and entropy to enhance influence maximization in social networks, outperforming existing approaches.

Contribution

The paper presents a novel community-structure-based influence maximization method combining K-shell decomposition and entropy to improve seed node selection.

Findings

Outperforms four state-of-the-art influence maximization methods

Validated on four public social network datasets

Efficient and effective in spreading information

Abstract

Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based approach that employs K-Shell algorithm with community structures to generate a score for the connections between seed nodes and communities. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We validate our approach on four publicly available networks and show its superiority to four state-of-the-art approaches while still being relatively efficient.