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Article Dans Une Revue ACM Transactions on Knowledge Discovery from Data (TKDD) Année : 2020

Span-core Decomposition for Temporal Networks: Algorithms and Applications

Edoardo Galimberti
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Martino Ciaperoni
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Francesco Bonchi
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Ciro Cattuto
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Francesco Gullo
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Résumé

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). In this paper we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores. For a temporal network defined on a discrete temporal domain T , the total number of time intervals included in T is quadratic in |T |, so that the total number of span-cores is potentially quadratic in |T | as well. Our first main 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 their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search, where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T. We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm. We provide an extensive experimentation on several real-world temporal networks of widely different origins and characteristics. Our results confirm the efficiency and scalability of the proposed methods. Moreover, we showcase the practical relevance of our techniques in a number of applications on temporal networks, describing face-to-face contacts between individuals in schools. Our experiments highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics, detecting/correcting anomalies in the data, and graph-embedding-based network classification. CCS Concepts: • Mathematics of computing → Graph algorithms; Graph theory; • Information systems → Temporal data; Data mining; • Theory of computation → Dynamic programming.
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Dates et versions

hal-03047191 , version 1 (16-12-2020)

Identifiants

Citer

Edoardo Galimberti, Martino Ciaperoni, Alain Barrat, Francesco Bonchi, Ciro Cattuto, et al.. Span-core Decomposition for Temporal Networks: Algorithms and Applications. ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 15 (1), pp.2. ⟨10.1145/3418226⟩. ⟨hal-03047191⟩
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