Rank-Refining Seed Selection Methods for Budget Constrained Influence Maximisation in Multilayer Networks under Linear Threshold Model

TL;DR

This paper evaluates seed selection methods for influence maximisation in multilayer networks under a Linear Threshold Model, highlighting the impact of various factors and proposing a new VoteRank variant for better diffusion.

Contribution

It introduces a comprehensive comparison of seed selection methods in multilayer networks with a Linear Threshold Model and extends centrality measures for this context.

Findings

VoteRank-based methods, especially v-rnk-m, often yield the best diffusion results.

Multiple factors, including network characteristics and model parameters, influence influence maximisation.

No single seed selection method consistently outperforms others across all scenarios.

Abstract

The problem of selecting an optimal seed set to maximise influence in networks has been a subject of intense research in recent years. However, despite numerous works addressing this area, it remains a topic that requires further elaboration. Most often, it is considered within the scope of classically defined graphs with a spreading model in the form of Independent Cascades. In this work, we focus on the problem of budget-constrained influence maximisation in multilayer networks using a Linear Threshold Model. Both the graph model and the spreading process we employ are less prevalent in the literature, even though their application allows for a more precise representation of the opinion dynamics in social networks. This paper aims to answer which of the sixteen evaluated seed selection methods is the most effective and how similar they are. Additionally, we focus our analysis on the impact of spreading model parameters, network characteristics, a budget, and the seed selection methods on the diffusion effectiveness in multilayer networks. Our contribution also includes extending several centrality measures and heuristics to the case of such graphs. The results indicate that all the factors mentioned above collectively contribute to the effectiveness of influence maximisation. Moreover, there is no seed selection method which always provides the best results. However, the seeds chosen with VoteRank-based methods (especially with the $v-rnk-m$ variant we propose) usually provide the most extensive diffusion.