A Spreader Ranking Algorithm for Extremely Low-budget Influence Maximization in Social Networks using Community Bridge Nodes

TL;DR

This paper introduces a community-aware influence maximization algorithm tailored for extremely low-budget scenarios, leveraging community bridge nodes and entropy measures to enhance information spread in social networks.

Contribution

It presents a novel community structure-based influence maximization method using K-Shell and entropy, improving efficiency and effectiveness over existing approaches in low-budget settings.

Findings

Outperforms baseline methods on four evaluation metrics

Effective in low-budget influence maximization scenarios

Validated on eight real-world social networks

Abstract

In recent years, social networking platforms have gained significant popularity among the masses like connecting with people and propagating ones thoughts and opinions. This has opened the door to user-specific advertisements and recommendations on these platforms, bringing along a significant focus on Influence Maximisation (IM) on social networks due to its wide applicability in target advertising, viral marketing, and personalized recommendations. The aim of IM is to identify certain nodes in the network which can help maximize the spread of certain information through a diffusion cascade. While several works have been proposed for IM, most were inefficient in exploiting community structures to their full extent. In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We choose the Independent Cascade (IC) model to simulate information spread and evaluate it on four evaluation metrics. We validate our proposed approach on eight publicly available networks and find that it significantly outperforms the baseline approaches on these metrics, while still being relatively efficient.