Density Decomposition on Hypergraphs

2026-04-09

AI Summary

This work addresses the limitations of existing hypergraph decomposition methods, which rely on vertex-degree constraints and struggle to accurately capture density variations induced by multi-way interactions, often yielding sparse or uneven decomposition hierarchies. To overcome this, the authors propose a novel integer-density-based $(k,\delta)$-dense subhypergraph model that enforces an upper bound on each hyperedge’s contribution to density, enabling fine-grained control over density distribution. By integrating a fairness-aware stability-oriented algorithm with a divide-and-conquer strategy, the single-layer mining complexity is reduced to $O(nm\delta)$, and the overall decomposition complexity is optimized to $O(nm\delta \cdot d^E_{\text{max}} \cdot \log k_{\text{max}})$. Experiments on nine real-world datasets demonstrate that the proposed method produces more continuous and less redundant hierarchies, effectively uncovering highly cohesive and interpretable community structures.

Abstract

Decomposing hypergraphs is a key task in hypergraph analysis with broad applications in community detection, pattern discovery, and task scheduling. Existing approaches such as $k$-core and neighbor-$k$-core rely on vertex degree constraints, which often fail to capture true density variations induced by multi-way interactions and may lead to sparse or uneven decomposition layers. To address these issues, we propose a novel \((k,δ)\)-dense subhypergraph model for decomposing hypergraphs based on integer density values. Here, $k$ represents the density level of a subhypergraph, while \(δ\) sets the upper limit for each hyperedge's contribution to density, allowing fine-grained control over density distribution across layers. Computing such dense subhypergraphs is algorithmically challenging, as it requires identifying an egalitarian orientation under bounded hyperedge contributions, which may incur an intuitive worst-case complexity of up to $O(2^{mδ})$. To enable efficient computation, we develop a fair-stable-based algorithm that reduces the complexity of mining a single $(k,δ)$-dense subhypergraph from $O(m^{2}δ^{2})$ to $O(nmδ)$. Building on this result, we further design a divide-and-conquer decomposition framework that improves the overall complexity of full density decomposition from $O(nmδ\cdot d^E_{\max} \cdot k_{\max})$ to $O(nmδ\cdot d^E_{\max} \cdot \log k_{\max})$. Experiments on nine real-world hypergraph datasets demonstrate that our approach produces more continuous and less redundant decomposition hierarchies than existing baselines, while maintaining strong computational efficiency. Case studies further illustrate the practical utility of our model by uncovering cohesive and interpretable community structures.

Problem

• hypergraph decomposition
• density variation
• multi-way interactions
• k-core
• community detection

Innovation

• hypergraph decomposition
• (k,δ)-dense subhypergraph
• density modeling
• fair-stable algorithm
• divide-and-conquer framework