Identifying Super Spreaders in Multilayer Networks

• URL: http://arxiv.org/abs/2505.20980v2
• Date: Thu, 31 Jul 2025 10:48:42 GMT
• Title: Identifying Super Spreaders in Multilayer Networks
• Authors: Michał Czuba, Mateusz Stolarski, Adam Piróg, Piotr Bielak, Piotr Bródka,
• Abstract summary: We introduce a novel approach to identifying super-spreaders in such networks by leveraging graph neural networks.<n>To this end, we construct a dataset by simulating information diffusion across hundreds of networks.<n>Our model, TopSpreadersNetwork, comprises a relationship-agnostic encoder and a custom aggregation layer.
• Score: 0.6990493129893112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying super-spreaders can be framed as a subtask of the influence maximisation problem. It seeks to pinpoint agents within a network that, if selected as single diffusion seeds, disseminate information most effectively. Multilayer networks, a specific class of heterogeneous graphs, can capture diverse types of interactions (e.g., physical-virtual or professional-social), and thus offer a more accurate representation of complex relational structures. In this work, we introduce a novel approach to identifying super-spreaders in such networks by leveraging graph neural networks. To this end, we construct a dataset by simulating information diffusion across hundreds of networks - to the best of our knowledge, the first of its kind tailored specifically to multilayer networks. We further formulate the task as a variation of the ranking prediction problem based on a four-dimensional vector that quantifies each agent's spreading potential: (i) the number of activations; (ii) the duration of the diffusion process; (iii) the peak number of activations; and (iv) the simulation step at which this peak occurs. Our model, TopSpreadersNetwork, comprises a relationship-agnostic encoder and a custom aggregation layer. This design enables generalisation to previously unseen data and adapts to varying graph sizes. In an extensive evaluation, we compare our model against classic centrality-based heuristics and competitive deep learning methods. The results, obtained across a broad spectrum of real-world and synthetic multilayer networks, demonstrate that TopSpreadersNetwork achieves superior performance in identifying high-impact nodes, while also offering improved interpretability through its structured output.

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