{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:30Z","timestamp":1758672930232,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Researchers, policy makers, and engineers need to make sense of data from spreading processes as diverse as \n\nrumor spreading in social networks, viral infections, and water contamination.\n\nClassical questions include predicting infection behavior in a given network or deducing the network structure from infection data.\n\nMost of the research on network infections studies static graphs, that is, the connections in the network are assumed to not change. \n\nMore recently, temporal graphs, in which connections change over time, have been used to more accurately represent real-world infections, which rarely occur in unchanging networks.\n\nWe propose a model for temporal graph discovery that is consistent with previous work on static graphs and embraces the greater expressiveness of temporal graphs.\n\nFor this model, we give algorithms and lower bounds which are often tight. We analyze different variations of the problem, which make our results widely applicable and it also clarifies which aspects of temporal infections make graph discovery easier or harder. \n\nWe round off our analysis with an experimental evaluation of our algorithm on real-world interaction data from the Stanford Network Analysis Project and on temporal Erd\u0151s-Renyi graphs.\n\nOn Erd\u0151s-Renyi graphs, we uncover a threshold behavior, which can be explained by a novel connectivity parameter that we introduce during our theoretical analysis.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/815","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7329-7337","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Network Discovery via Infection Tracing"],"prefix":"10.24963","author":[{"given":"Ben","family":"Bals","sequence":"first","affiliation":[{"name":"Centrum Wiskunde & Informatica, Amsterdam, The Netherlands"},{"name":"Vrije Universiteit Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelle","family":"D\u00f6ring","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Klodt","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Skretas","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:12Z","timestamp":1758627312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/815"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/815","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}