• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 19 Feb 2026] Title:Distributed Triangle Enumeration in Hypergraphs View PDF HTML (experimental)Abstract:In the last decade, subgraph detection and enumeration have emerged as a central problem in distributed graph algorithms. • This is largely due to the theoretical challenges and practical applications of these problems. • In this paper, we initiate the systematic study of distributed sub-hypergraph enumeration in hypergraphs. • To this end, we (1)~introduce several computational models for hypergraphs that generalize the CONGEST model for graphs and evaluate their relative computational power, (2)~devise algorithms for distributed triangle enumeration in our computational models and prove their optimality in two such models, (3)~introduce classes of sparse and ``everywhere sparse’’ hypergraphs and describe efficient distributed algorithms for triangle enumeration in these classes, and (4)~describe general techniques that we believe to be useful for designing efficient algorithms in our hypergraph models. • References & Citations export BibTeX citation Loading… • Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What
Article Summaries:
- Distributed Triangle Enumeration in Hypergraphs
A recent study extends distributed graph algorithms to hypergraphs, focusing on triangle enumeration. The authors introduce several hypergraph computational models that generalize the classic CONGEST model, assessing their relative power. They design algorithms for triangle enumeration within these models and prove optimality in two of them. Additionally, the paper defines sparse and “everywhere sparse” hypergraph classes and presents efficient enumeration methods tailored to these structures. Finally, it outlines general techniques that could guide future algorithmic developments in hypergraph settings. The work provides a foundational framework for distributed sub‑hypergraph detection and sets the stage for further research in this emerging area.
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