Nucleus Decomposition Revisited: An Efficient Counting-Based Approach

Nucleus decomposition provides a unified framework for discovering hierarchically cohesive substructures in graphs by generalizing the notions of k -core and k -truss to higher-order ( r,s )-nucleus. Existing algorithms suffer from a fundamental bottleneck: they rely on explicit enumeration of all s -cliques, whose number grows combinatorially with s . In this paper, we revisit the existing nucleus decomposition framework and present the first counting-based approach that eliminates explicit s -clique enumeration. We propose the Clique Path Index, a compact auxiliary structure that encodes the s -clique search space as concise paths, enabling direct computation, efficient dynamic updates, and connectivity maintenance during nucleus decomposition. Extensive experiments on real-world datasets demonstrate that our approach achieves an average speedup of one order of magnitude and up to two orders of magnitude for both nucleus decomposition and hierarchy construction. More importantly, while existing algorithms often time out even for small values of s , our method scales efficiently to larger s and denser graphs.

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2026-05-18
DOI
https://doi.org/10.1145/3802092
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Nucleus Decomposition Revisited: An Efficient Counting-Based Approach

Wenqian Zhang, Yi Ding, X L Lin, Zhengyi Yang et al.
Proceedings of the ACM on Management of Data
Advanced Graph Neural Networks
article

Nucleus Decomposition Revisited: An Efficient Counting-Based Approach

Wenqian Zhang, Yi Ding, X L Lin, Zhengyi Yang, Wenjie Zhang, Dong Wen
article en

Abstract

Nucleus decomposition provides a unified framework for discovering hierarchically cohesive substructures in graphs by generalizing the notions of k -core and k -truss to higher-order ( r,s )-nucleus. Existing algorithms suffer from a fundamental bottleneck: they rely on explicit enumeration of all s -cliques, whose number grows combinatorially with s . In this paper, we revisit the existing nucleus decomposition framework and present the first counting-based approach that eliminates explicit s -clique enumeration. We propose the Clique Path Index, a compact auxiliary structure that encodes the s -clique search space as concise paths, enabling direct computation, efficient dynamic updates, and connectivity maintenance during nucleus decomposition. Extensive experiments on real-world datasets demonstrate that our approach achieves an average speedup of one order of magnitude and up to two orders of magnitude for both nucleus decomposition and hierarchy construction. More importantly, while existing algorithms often time out even for small values of s , our method scales efficiently to larger s and denser graphs.

Proceedings of the ACM on Management of DataVol. 4(3)
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 4%
Advanced Graph Neural Networks
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Nucleus Decomposition Revisited: An Efficient Counting-Based Approach — Wenqian Zhang, Yi Ding, et al. · Proceedings of the ACM on Management of Data (2026) | TGRS Research Map | TGRS