Large-scale multi-objective influence maximisation with network downscaling

TL;DR

This paper introduces a network downscaling approach combined with multi-objective evolutionary algorithms to efficiently identify influential nodes in large networks, significantly reducing computation time while maintaining accuracy.

Contribution

The paper presents a novel network downscaling method that enables scalable influence maximization using MOEA, with effective upscaling to original networks.

Findings

Over 10-fold runtime reduction compared to original network methods

Up to 82% time reduction compared to CELF algorithm

Effective influence maximization on networks with around 50,000 nodes

Abstract

Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several methods have been proposed for tackling the influence maximisation (IM) problem, their runtime typically scales poorly when the network size increases. Here, we propose an original method, based on network downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to solve the IM problem on a reduced scale network, while preserving the relevant properties of the original network. The downscaled solution is then upscaled to the original network, using a mechanism based on centrality metrics such as PageRank. Our results on eight large networks (including two with $\sim$50k nodes) demonstrate the effectiveness of the proposed method with a more than 10-fold runtime gain compared to the time needed on the original network, and an up to $82\%$ time reduction compared to CELF.