Mining (maximal) span-cores from temporal networks

Abstract : When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain T under analysis, the total number of span-cores is quadratic in |T | as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.
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Contributeur : Alain Barrat <>
Soumis le : mercredi 29 août 2018 - 11:23:12
Dernière modification le : vendredi 7 septembre 2018 - 01:19:16
Document(s) archivé(s) le : vendredi 30 novembre 2018 - 14:17:32


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Edoardo Galimberti, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Francesco Gullo. Mining (maximal) span-cores from temporal networks. CIKM2018, Oct 2018, Torino, Italy. 〈10.1145/3269206.3271767〉. 〈hal-01863956〉



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