Article

GraphScope: parameter-free mining of large time-evolving graphs

Authors:

Christos Faloutsos,

Spiros Papadimitriou,

Philip S. YuAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 687 - 696

https://doi.org/10.1145/1281192.1281266

Published: 12 August 2007 Publication History

Abstract

How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition.

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Cited By

Zhou YGao SGuo DWei XRokne JWang H(2025)A Survey of Change Point Detection in Dynamic GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352385737:3(1030-1048)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3523857
Ai YXie XMa X(2024)Graph Contrastive Learning for Tracking Dynamic Communities in Temporal NetworksIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33868448:5(3422-3435)Online publication date: Oct-2024
https://doi.org/10.1109/TETCI.2024.3386844
Zhao ZLi WWang XMeng XZheng XWang C(2024)Structural Graph Clustering on Signed Graphs: An Index-based ApproachInformation Sciences10.1016/j.ins.2024.121766(121766)Online publication date: Dec-2024
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Index Terms

GraphScope: parameter-free mining of large time-evolving graphs
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1109/TKDE.2024.3523857
Ai YXie XMa X(2024)Graph Contrastive Learning for Tracking Dynamic Communities in Temporal NetworksIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33868448:5(3422-3435)Online publication date: Oct-2024
https://doi.org/10.1109/TETCI.2024.3386844
Zhao ZLi WWang XMeng XZheng XWang C(2024)Structural Graph Clustering on Signed Graphs: An Index-based ApproachInformation Sciences10.1016/j.ins.2024.121766(121766)Online publication date: Dec-2024
https://doi.org/10.1016/j.ins.2024.121766
Christopoulos KTsichlas K(2024)Local Community-Based Anomaly Detection in Graph StreamsArtificial Intelligence Applications and Innovations10.1007/978-3-031-63211-2_26(348-361)Online publication date: 21-Jun-2024
https://doi.org/10.1007/978-3-031-63211-2_26
Zhou DHe J(2023)Rare Category Analysis for Complex Data: A ReviewACM Computing Surveys10.1145/362652056:5(1-35)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3626520
Wendt JField RPhillips CPrasadan AWilson TSoundarajan SBhowmick S(2023)Partitioning Communication Streams Into Graph SnapshotsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.322361410:2(809-826)Online publication date: 1-Mar-2023
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Jiao PLi TWu HWang CHe DWang W(2023)HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node LevelIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.314928534:11(8310-8323)Online publication date: Nov-2023
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Li DMa XGong M(2023)Joint Learning of Feature Extraction and Clustering for Large-Scale Temporal NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2021.310767953:3(1653-1666)Online publication date: Mar-2023
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Sharma KSingh A(2023)A Systematic Review: Detection of Anomalies in Social Networks2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)10.1109/ICSCDS56580.2023.10104612(1470-1476)Online publication date: 23-Mar-2023
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Fukushima SYamanishi K(2023)Balancing Summarization and Change Detection in Graph Streams2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00118(1025-1030)Online publication date: 1-Dec-2023
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