Article

Mining closed relational graphs with connectivity constraints

Authors:

X. Jasmine Zhou,

Jiawei HanAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 324 - 333

https://doi.org/10.1145/1081870.1081908

Published: 21 August 2005 Publication History

Abstract

Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In this kind of graph, connectivity becomes critical in identifying highly associated groups and clusters. In this paper, we investigate the issues of mining closed frequent graphs with connectivity constraints in massive relational graphs where each graph has around 10K nodes and 1M edges. We adopt the concept of edge connectivity and apply the results from graph theory, to speed up the mining process. Two approaches are developed to handle different mining requests: CloseCut, a pattern-growth approach, and splat, a pattern-reduction approach. We have applied these methods in biological datasets and found the discovered patterns interesting.

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Cited By

Qiao LLi RZhang ZYuan YWang GQin H(2023)Maximal Quasi-Cliques Mining in Uncertain GraphsIEEE Transactions on Big Data10.1109/TBDATA.2021.30933559:1(37-50)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2021.3093355
Chang LWang Z(2023)A near-optimal approach to edge connectivity-based hierarchical graph decompositionThe VLDB Journal10.1007/s00778-023-00797-x33:1(49-71)Online publication date: 6-May-2023
https://doi.org/10.1007/s00778-023-00797-x
Chang LWang Z(2022)A near-optimal approach to edge connectivity-based hierarchical graph decompositionProceedings of the VLDB Endowment10.14778/3514061.351406315:6(1146-1158)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.14778/3514061.3514063
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Index Terms

Mining closed relational graphs with connectivity constraints
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Copyright © 2005 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: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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

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Cited By

Qiao LLi RZhang ZYuan YWang GQin H(2023)Maximal Quasi-Cliques Mining in Uncertain GraphsIEEE Transactions on Big Data10.1109/TBDATA.2021.30933559:1(37-50)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2021.3093355
Chang LWang Z(2023)A near-optimal approach to edge connectivity-based hierarchical graph decompositionThe VLDB Journal10.1007/s00778-023-00797-x33:1(49-71)Online publication date: 6-May-2023
https://doi.org/10.1007/s00778-023-00797-x
Chang LWang Z(2022)A near-optimal approach to edge connectivity-based hierarchical graph decompositionProceedings of the VLDB Endowment10.14778/3514061.351406315:6(1146-1158)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.14778/3514061.3514063
Ke XKhan ABonchi F(2022)Multi-relation Graph SummarizationACM Transactions on Knowledge Discovery from Data10.1145/349456116:5(1-30)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3494561
Liu MLi P(2022)SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space SamplingProceedings of the ACM Web Conference 202210.1145/3485447.3512196(1495-1505)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512196
Sun RWu YChen CWang XZhang WLin X(2022)Maximal Balanced Signed Biclique Enumeration in Signed Bipartite Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00187(1887-1899)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00187
Fang YWang KLin XZhang WFang YWang KLin XZhang W(2022)Related Work on CSMs and SolutionsCohesive Subgraph Search Over Large Heterogeneous Information Networks10.1007/978-3-030-97568-5_6(57-60)Online publication date: 23-Feb-2022
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Meng SYang HLiu XChen ZXuan JWu Y(2021)Personalized Influential Community Search in Large Networks: A K-ECC-Based ModelDiscrete Dynamics in Nature and Society10.1155/2021/53639462021(1-10)Online publication date: 29-Nov-2021
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Jazayeri AYang C(2021)Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A SurveyIEEE Transactions on Big Data10.1109/TBDATA.2021.3072001(1-1)Online publication date: 2021
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Elhesha RSarkar AKahveci T(2021)Motifs in Biological NetworksRecent Advances in Biological Network Analysis10.1007/978-3-030-57173-3_5(101-123)Online publication date: 14-Jan-2021
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