Discovering Dense Correlated Subgraphs in Dynamic Networks
Preti, Giulia; Rozenshtein, Polina; Gionis, Aristides; Velegrakis, Yannis; Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Reddy, P. Krishna; Srivastava, Jaideep; Chakraborty, Tanmoy
(2021)
Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings, volume 12712 LNAI, pp. 395 - 407
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12712 LNAI, pp. 395 - 407
25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021, volume 12712 LNAI, pp. 395 - 407
(Part of book)
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
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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.
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Keywords: Theoretical Computer Science, Computer Science(all)
ISSN: 0302-9743
ISBN: 9783030757618
Publisher: Springer Science and Business Media Deutschland GmbH
Note: Funding Information: 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). Publisher Copyright: © 2021, Springer Nature Switzerland AG.
(Peer reviewed)
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