{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:32Z","timestamp":1761176192305,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>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 \u2014 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\u2019s 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.<\/jats:p>","DOI":"10.3233\/faia251060","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:16Z","timestamp":1761126616000},"source":"Crossref","is-referenced-by-count":0,"title":["Identifying Super Spreaders in Multilayer Networks"],"prefix":"10.3233","author":[{"given":"Micha\u0142","family":"Czuba","sequence":"first","affiliation":[{"name":"Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mateusz","family":"Stolarski","sequence":"additional","affiliation":[{"name":"Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Pir\u00f3g","sequence":"additional","affiliation":[{"name":"4Semantics, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Bielak","sequence":"additional","affiliation":[{"name":"Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Br\u00f3dka","sequence":"additional","affiliation":[{"name":"Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251060","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:16Z","timestamp":1761126616000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251060","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}