Maximal Clique Enumeration with Hybrid Branching and Early Termination
Kaixin Wang, Kaiqiang Yu, Cheng Long
Abstract
Maximal clique enumeration (MCE) is crucial for tasks like community detection and biological network analysis. Existing algorithms typically adopt the branch-and-bound frame-work with the vertex-oriented Bron-Kerbosch (BK) branching strategy, which forms the sub-branches by expanding the partial clique with a vertex. In this paper, we present a novel approach, HBBMC, a hybrid framework combining vertex-oriented BK branching and edge-oriented BK branching, where the latter adopts a branch-and-bound framework which forms the sub-branches by expanding the partial clique with a edge. This hybrid strategy enables more effective pruning and helps achieve a worst-case time complexity better than the best-known one under a condition which holds for the majority of real-world graphs. To further enhance efficiency, we introduce an early termination technique, which leverages the topological information of the graphs and constructs the maximal cliques directly without branching. Our early termination technique is applicable to all branch-and-bound frameworks. Extensive experiments demonstrate the superior performance of our techniques.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c2c8c263-3a24-43de-80b6-1ed75eaf324aCited by top-tier papers3
- Efficient Defective Clique Enumeration and Search with Worst-Case Optimal Search SpaceJihoon Jang, Yehyun Nam, Kunsoo Park, Hyunjoon KimSIGMOD 2026 · 1 citation
- Aggregating maximal cliques in real-world graphsNoga Alon, Sabyasachi Basu, Shweta Jain, Haim Kaplan et al.VLDB 2026
- Maximal Biclique Enumeration with Improved Worst-Case Time Complexity Guarantee: A Partition-Oriented StrategyKaixin Wang, Kaiqiang Yu, Cheng LongSIGMOD 2026
Builds on9
- Efficient Maximal Biclique Enumeration for Large Sparse Bipartite GraphsLu Chen, Chengfei Liu, Rui Zhou, Jiajie Xu et al.VLDB 2022 · 62 citations
- Accelerating Truss Decomposition on Heterogeneous ProcessorsYulin Che, Zhuohang Lai, Shixuan Sun, Yue Wang et al.VLDB 2020 · 46 citations
- Fast Maximal Quasi-clique Enumeration: A Pruning and Branching Co-Design ApproachKaiqiang Yu, Cheng LongSIGMOD 2024 · 23 citations
- Fast Maximal Clique Enumeration on Uncertain Graphs: A Pivot-based ApproachQiangqiang Dai, Rong-Hua Li, Meihao Liao, Hongzhi Chen et al.SIGMOD 2022 · 22 citations
- Efficient k-Clique Listing: An Edge-Oriented Branching StrategyKaixin Wang, Kaiqiang Yu, Cheng LongSIGMOD 2024 · 20 citations
Related papers
- More Than Pivot for Maximal Clique EnumerationZhaoyi Zhong, Rui Zhou, Lu Chen, Xiaofan Li et al.ICDE 2026
- Theoretically and Practically Efficient Maximum Biclique SearchQiangqiang Dai, Rong-Hua Li, Lianpeng Qiao, Donghang Cui et al.SIGMOD 2026
- Efficient Maximal Biplex Enumerations with Improved Worst-Case Time GuaranteeQiangqiang Dai, Rong-Hua Li, Donghang Cui, Meihao Liao et al.SIGMOD 2024 · 6 citations
- Maximum Balanced Clique Search on Large Directed GraphsJianhua Wang, Jianye Yang, Zhaoquan Gu, Dian Ouyang et al.ICDE 2026
- Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksYingli Zhou, Yixiang Fang, Chenhao Ma, Tianci Hou et al.VLDB 2024 · 15 citations
