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Mining Largest Maximal Quasi-Cliques

Authors:

Seyed-Vahid Sanei-Mehri,

Hooman Hashemi,

Srikanta TirthapuraAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 5

Article No.: 81, Pages 1 - 21

https://doi.org/10.1145/3446637

Published: 15 April 2021 Publication History

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Abstract

Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating quasi-cliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top-k degree-based quasi-cliques and make the following contributions: (1) we show that even the problem of detecting whether a given quasi-clique is maximal (i.e., not contained within another quasi-clique) is NP-hard. (2) We present a novel heuristic algorithm KernelQC to enumerate the k largest quasi-cliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, followed by growing subgraphs around these kernels, to arrive at quasi-cliques with the required densities. (3) Experimental results show that our algorithm accurately enumerates quasi-cliques from a graph, is much faster than current state-of-the-art methods for quasi-clique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.

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Index Terms

Mining Largest Maximal Quasi-Cliques
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 5

October 2021

508 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3461317

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 15 April 2021

Accepted: 01 December 2020

Revised: 01 November 2020

Received: 01 April 2020

Published in TKDD Volume 15, Issue 5

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Hsu BChen YHo YChang PChang CShia BShen C(2024)Diversity-Optimized Group Extraction in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322493511:1(756-769)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3224935
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https://dl.acm.org/doi/10.1016/j.eswa.2023.121816
Pal B(2024)A Recursive Approach for Maximal ()-Clique Enumeration in Temporal NetworksAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_6(79-92)Online publication date: 28-Aug-2024
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Yu KLong C(2023)Fast Maximal Quasi-clique Enumeration: A Pruning and Branching Co-Design ApproachProceedings of the ACM on Management of Data10.1145/36173311:3(1-26)Online publication date: 13-Nov-2023
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Zhou JLiu SGao J(2022)A local search algorithm with hybrid strategies for the maximum weighted quasi‐clique problemElectronics Letters10.1049/ell2.1268559:1Online publication date: 7-Dec-2022
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