Abstract
Given a dynamic network, where edges appear and disappear over time, we are interested in finding sets of edges that have similar temporal behavior and form a dense subgraph. Formally, we define the problem as the enumeration of the maximal subgraphs that satisfy specific density and similarity thresholds. To measure the similarity of the temporal behavior, we use the correlation between the binary time series that represent the activity of the edges. For the density, we study two variants based on the average degree. For these problem variants we enumerate the maximal subgraphs and compute a compact subset of subgraphs that have limited overlap. We propose an approximate algorithm that scales well with the size of the network, while achieving a high accuracy. We evaluate our framework on both real and synthetic datasets. The results of the synthetic data demonstrate the high accuracy of the approximation and show the scalability of the framework.
P. Rozenshtein—Work done while at Aalto University, Finland and IDS, NUS, Singapore.
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Acknowledgments
Aristides Gionis and Giulia Preti are supported by EC H2020 RIA project “SoBigData++” (871042). Aristides Gionis is supported by three Academy of Finland projects (286211, 313927, 317085), the ERC Advanced Grant REBOUND (834862), the Wallenberg AI, Autonomous Systems and Software Program (WASP).
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Preti, G., Rozenshtein, P., Gionis, A., Velegrakis, Y. (2021). Discovering Dense Correlated Subgraphs in Dynamic Networks. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_32
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