Covering K-Cliques in Billion-Scale Graphs

The k-clique structure in graphs has been investigated in various real-world applications, such as community detection in complex networks, functional module discovery in biological networks, and link spam detection in web graphs. Despite extensive research on k-clique enumeration, the large number of k-cliques in many graphs poses a challenge for practical application and computation. To address this, we explore the k-clique τ-cover problem, a generalization of the vertex cover problem. The problem aims to find a small set of vertices that can effectively represent all k-cliques in the graph. We prove the NP-hardness of finding the minimum k-clique cover. We propose a hierarchical solution that computes a small cover without enumerating k-cliques. Extensive experiments on real-world graphs verify the efficiency and effectiveness of our solution.

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Published
2025-04-22
DOI
https://doi.org/10.1145/3696410.3714897
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Covering K-Cliques in Billion-Scale Graphs

Zhengyi Yang, Kaiyu Chen, Xuemin Lin, Wenjie Zhang et al.
Complex Network Analysis Techniques
article

Covering K-Cliques in Billion-Scale Graphs

Zhengyi Yang, Kaiyu Chen, Xuemin Lin, Wenjie Zhang, Hanchen Wang, Dong Wen
article en

Abstract

The k-clique structure in graphs has been investigated in various real-world applications, such as community detection in complex networks, functional module discovery in biological networks, and link spam detection in web graphs. Despite extensive research on k-clique enumeration, the large number of k-cliques in many graphs poses a challenge for practical application and computation. To address this, we explore the k-clique τ-cover problem, a generalization of the vertex cover problem. The problem aims to find a small set of vertices that can effectively represent all k-cliques in the graph. We prove the NP-hardness of finding the minimum k-clique cover. We propose a hierarchical solution that computes a small cover without enumerating k-cliques. Extensive experiments on real-world graphs verify the efficiency and effectiveness of our solution.

University of Technology Sydney (AU), Shanghai Jiao Tong University (CN), UNSW Sydney (AU)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 93%
Complex Network Analysis Techniques
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Covering K-Cliques in Billion-Scale Graphs — Zhengyi Yang, Kaiyu Chen, et al. · (2025) | TGRS Research Map | TGRS