Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach

TL;DR

This paper introduces hop-based algorithms for influence maximization in large-scale social networks, offering a scalable, efficient, and theoretically-guaranteed alternative to existing methods.

Contribution

It proposes a novel hop-based approach that scales to millions of nodes and provides theoretical guarantees, addressing limitations of prior heuristic and simulation-based methods.

Findings

Algorithms scale to millions of nodes and billions of edges.

Experimental results show improved efficiency and effectiveness.

Theoretical guarantees differentiate from previous heuristics.

Abstract

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.