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

2024-05-28

arXiv.org

AI Summary

This study addresses the budget-constrained influence maximization problem in multilayer networks under the Linear Threshold model. We systematically evaluate 16 seed selection methods, extending single-layer centrality measures—including Degree, Betweenness, and VoteRank—as well as heuristic strategies to the multilayer setting, and propose an improved algorithm, v-rnk-m. Using Monte Carlo simulations and parameter sensitivity analysis, we uncover significant interaction effects among activation threshold distribution, interlayer coupling strength, and budget size. Our results demonstrate that no universally optimal method exists across diverse multilayer topologies; instead, v-rnk-m achieves an average 12.7% improvement in diffusion coverage over baseline methods, exhibiting superior robustness and generalizability across heterogeneous multilayer networks.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Optimizing seed selection for influence maximization in multilayer networks
• Comparing effectiveness of 16 seed selection methods under budget constraints
• Extending centrality measures for Linear Threshold Model in multilayer graphs

Innovation

• Uses Linear Threshold Model in multilayer networks
• Extends centrality measures for multilayer graphs
• Proposes VoteRank-based seed selection methods