The Power of Core Clique Removal for Exact Clique Enumeration

Clique enumeration, including maximal clique enumeration and k -clique enumeration, is a fundamental problem in graph analysis. However, existing clique enumeration algorithms often struggle to efficiently handle complex real-world graphs. To address this issue, we propose a novel Core Clique Removal (CCR) technique that removes a large core clique from the original graph. After removing the core clique, the remaining graph is expected to become sparser. We show that the clique enumeration problem on the original graph can be reduced to the problem of enumerating cliques in the remaining sparser graph, thus reducing computational complexity. Building upon the CCR technique, we propose two innovative and efficient algorithms: CCRMCE for maximal clique enumeration and CCRKCE for k -clique enumeration. Both algorithms are non-trivial and offer substantial improvements over previous approaches. Our extensive experiments on 20 real-world graphs highlight the efficiency of our methods. CCRMCE demonstrates multiple times faster performance, while CCRKCE achieves an order of magnitude speedup compared to state-of-the-art algorithms.

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2025-09-22
DOI
https://doi.org/10.1145/3749187
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

The Power of Core Clique Removal for Exact Clique Enumeration

Proceedings of the ACM on Management of Data
Geochemistry and Geologic Mapping
article

The Power of Core Clique Removal for Exact Clique Enumeration

article en

Abstract

Clique enumeration, including maximal clique enumeration and k -clique enumeration, is a fundamental problem in graph analysis. However, existing clique enumeration algorithms often struggle to efficiently handle complex real-world graphs. To address this issue, we propose a novel Core Clique Removal (CCR) technique that removes a large core clique from the original graph. After removing the core clique, the remaining graph is expected to become sparser. We show that the clique enumeration problem on the original graph can be reduced to the problem of enumerating cliques in the remaining sparser graph, thus reducing computational complexity. Building upon the CCR technique, we propose two innovative and efficient algorithms: CCRMCE for maximal clique enumeration and CCRKCE for k -clique enumeration. Both algorithms are non-trivial and offer substantial improvements over previous approaches. Our extensive experiments on 20 real-world graphs highlight the efficiency of our methods. CCRMCE demonstrates multiple times faster performance, while CCRKCE achieves an order of magnitude speedup compared to state-of-the-art algorithms.

Proceedings of the ACM on Management of DataVol. 3(4)
Beijing Institute of Technology (CN)
Openalex Percentile: Top 30%
Geochemistry and Geologic Mapping
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