research-article

Scaling Up k-Clique Densest Subgraph Detection

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Ying ZhangAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 69, Pages 1 - 26

https://doi.org/10.1145/3588923

Published: 30 May 2023 Publication History

Abstract

In this paper, we study the k-clique densest subgraph problem, which detects the subgraph that maximizes the ratio between the number of k-cliques and the number of vertices in it. The problem has been extensively studied in the literature and has many applications in a wide range of fields such as biology and finance. Existing solutions rely heavily on repeatedly computing all the k-cliques, which are not scalable to handle large k values on large-scale graphs. In this paper, by adapting the idea of "pivoting", we propose the SCT*-Index to compactly organize the k-cliques. Based on the SCT*-Index, our SCTL algorithm can directly obtain the k-cliques from the index and efficiently achieve near-optimal approximation. To further improve SCTL, we propose SCTL* that includes novel graph reductions and batch-processing optimizations to reduce the search space and decrease the number of visited k-cliques, respectively. As evaluated in our experiments, SCTL* significantly outperform existing approaches by up to two orders of magnitude. In addition, we propose a sampling-based approximate algorithm that can provide reasonable approximations for any k value on billion-scale graphs. Extensive experiments on 12 real-world graphs validate both the efficiency and effectiveness of the proposed techniques.

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Cited By

Chang LGamage RYu J(2024)Efficient k-Clique Count Estimation with Accuracy GuaranteeProceedings of the VLDB Endowment10.14778/3681954.368203217:11(3707-3719)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682032
Zhang YLi RZhang QQin HWang G(2024)Efficient Algorithms for Density Decomposition on Large Static and Dynamic GraphsProceedings of the VLDB Endowment10.14778/3681954.368197417:11(2933-2945)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681974
Deng WZheng WCheng H(2024)Accelerating Maximal Clique Enumeration via Graph ReductionProceedings of the VLDB Endowment10.14778/3675034.367503617:10(2419-3431)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3675034.3675036
Show More Cited By

Index Terms

Scaling Up k-Clique Densest Subgraph Detection
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Network data models

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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Cited By

Chang LGamage RYu J(2024)Efficient k-Clique Count Estimation with Accuracy GuaranteeProceedings of the VLDB Endowment10.14778/3681954.368203217:11(3707-3719)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682032
Zhang YLi RZhang QQin HWang G(2024)Efficient Algorithms for Density Decomposition on Large Static and Dynamic GraphsProceedings of the VLDB Endowment10.14778/3681954.368197417:11(2933-2945)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681974
Deng WZheng WCheng H(2024)Accelerating Maximal Clique Enumeration via Graph ReductionProceedings of the VLDB Endowment10.14778/3675034.367503617:10(2419-3431)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3675034.3675036
Zhou YGuo QFang YMa C(2024)A Counting-based Approach for Efficient k-Clique Densest Subgraph DiscoveryProceedings of the ACM on Management of Data10.1145/36549222:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654922
Lin LJia TWang ZZhao JLi RBaeza-Yates RBonchi F(2024)PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph ClusteringProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671666(1793-1803)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671666
Li SWang KLin XZhang WHe YYuan L(2024)Querying Historical Cohesive Subgraphs Over Temporal Bipartite Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00197(2503-2516)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00197
Sun HLi XWu ZSu DLi RWang G(2024)Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00188(2379-2392)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00188
Xu YMa CFang YBao Z(2024)Efficient and effective algorithms for densest subgraph discovery and maintenanceThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00855-y33:5(1427-1452)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1007/s00778-024-00855-y

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