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arxiv: 2606.18131 · v1 · pith:V5RZL63Dnew · submitted 2026-06-16 · 💻 cs.AR

ComPart: Community-Guided Post-Coarsening for High-Quality Hypergraph Partitioning

Yugao Zhu , Zhicheng Guo , Yuchao Wu , Mengming Li , Jing Wang , Zhiyao Xie This is my paper

Pith reviewed 2026-06-26 21:50 UTC · model grok-4.3

classification 💻 cs.AR
keywords hypergraph partitioningcommunity detectioncoarseninguncoarseninglocally-dense decompositionrefinementMPSoCFPGA prototyping
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The pith

ComPart integrates community detection after coarsening to guide refinements and improve hypergraph partition quality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ComPart as a framework that applies community detection not only in coarsening but also in initial partitioning and uncoarsening stages of hypergraph partitioning. These detected clusterings act as structural guides to help the refinement process move beyond local optima toward better global solutions. The work also provides a formal generalization of locally-dense decomposition from graphs to hypergraphs, using it to improve starting points for partitioning. This matters for applications such as task mapping on heterogeneous MPSoCs and multi-FPGA prototyping, where partition quality directly affects system performance. If the approach holds, it offers a flexible way to incorporate various community detection methods into the post-coarsening process.

Core claim

ComPart establishes a new paradigm that leverages community structures detected during uncoarsening to escape local optima and explore globally meaningful solution subspaces, while generalizing locally-dense decomposition to the hypergraph domain to guide initial partitioning, and experimental results on standard benchmarks show consistent outperformance of state-of-the-art methods.

What carries the argument

The ComPart framework that detects community structures in post-coarsening phases to provide structural guidance for refinement and initial partitioning.

If this is right

  • Community structures from uncoarsening enable escape from local optima beyond standard local refinements.
  • The framework flexibly accommodates both existing and future community detection methods.
  • The generalized locally-dense decomposition guides initial partitioning toward superior starting points.
  • The approach yields higher-quality partitions on standard benchmarks compared to prior methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The post-coarsening guidance idea could extend to other multilevel optimization problems that use coarsening-uncoarsening cycles.
  • Different community detectors might be paired with specific hypergraph types to further tune partition quality.
  • The method's emphasis on global subspaces suggests potential for hybrid approaches combining it with global search techniques.

Load-bearing premise

Community structures detected by arbitrary methods during uncoarsening reliably improve global partition quality without introducing bias or excessive computation.

What would settle it

If experiments on standard hypergraph benchmarks show no consistent quality improvement over existing methods, or if the hypergraph extension of locally-dense decomposition fails to yield better starting points, the central claims would not hold.

Figures

Figures reproduced from arXiv: 2606.18131 by Jing Wang, Mengming Li, Yuchao Wu, Yugao Zhu, Zhicheng Guo, Zhiyao Xie.

Figure 1
Figure 1. Figure 1: Framework Comparison between our method Com [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed ComPart framework. Locally-dense decomposition is used for community detection in the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Different Commu￾nity Detection Strategies. 4.2 Comprehensive performance on standard benchmarks 4.2.1 ComPart against Baselines on Titan23 Benchmark [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Hypergraph partitioning is a critical step in the design of complex embedded systems, essential for optimizing task mapping on heterogeneous MPSoCs and enabling multi-FPGA prototyping. Many existing methods rely on community detection to identify modules with dense internal and sparse external connections, typically utilizing them to constrain the coarsening phase--a widely adopted paradigm. In this work, we propose ComPart, a generalized framework that integrates diverse community detection methods to uncover high-quality clusterings throughout the post-coarsening stages (i.e., initial partitioning and uncoarsening). These discovered clusterings serve as distinct structural guides, enabling the refinement process to identify superior partitioning solutions. Our framework offers two key advantages: (1) it establishes a new paradigm that leverages community structures detected during uncoarsening to escape local optima and explore globally meaningful solution subspaces, transcending the limitations of standard local refinements; and (2) it flexibly accommodates both existing and future community detection methods. Furthermore, we theoretically generalize locally-dense decomposition--originally from graphs--to the hypergraph domain. We provide the formal extension and necessary proofs to apply this technique to hypergraphs, marking its first application in hypergraph partitioning. Specifically, we utilize this rigorously derived decomposition to guide the initial partitioning phase toward superior starting points. Experimental results on standard benchmarks demonstrate that our method consistently outperforms state-of-the-art methods in solution quality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes ComPart, a generalized framework for hypergraph partitioning that applies community detection methods during post-coarsening stages (initial partitioning and uncoarsening) to provide structural guidance for refinement, thereby escaping local optima. It also presents a formal generalization of locally-dense decomposition from graphs to hypergraphs, including proofs, and uses this to guide initial partitioning. Experiments on standard benchmarks claim consistent quality improvements over state-of-the-art methods for applications such as MPSoC task mapping and multi-FPGA prototyping.

Significance. If the central claims hold, the work introduces a new paradigm for leveraging community structures in uncoarsening to improve global partition quality beyond standard local search, with potential impact on embedded system design. The theoretical generalization of locally-dense decomposition to hypergraphs, with provided proofs and first application to partitioning, represents a clear methodological contribution that could be adopted by future methods.

major comments (2)
  1. [§5] §5 (Experimental Results): The manuscript reports consistent outperformance over SOTA but does not describe an ablation that disables only the community-guided refinement component during uncoarsening while holding fixed the generalized locally-dense decomposition, coarsening, and other refinements. Without this isolation, it is impossible to verify that the reported quality gains are driven by the claimed new paradigm rather than by the initial-partitioning step or implementation details.
  2. [§3, §4] §3 (Framework Description) and §4 (Theoretical Generalization): The central claim that community structures detected by arbitrary methods during uncoarsening reliably improve global quality without introducing bias rests on the assumption that these structures provide structural guidance superior to standard local refinements; however, no quantitative analysis or counterexample analysis is provided to bound the conditions under which this holds.
minor comments (1)
  1. The abstract and introduction would benefit from explicit citation of the specific community detection methods tested and the hypergraph benchmarks used, to allow immediate reproducibility assessment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for clearer isolation of contributions and analysis of conditions for the community-guided approach. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§5] §5 (Experimental Results): The manuscript reports consistent outperformance over SOTA but does not describe an ablation that disables only the community-guided refinement component during uncoarsening while holding fixed the generalized locally-dense decomposition, coarsening, and other refinements. Without this isolation, it is impossible to verify that the reported quality gains are driven by the claimed new paradigm rather than by the initial-partitioning step or implementation details.

    Authors: We agree that isolating the effect of community-guided refinement specifically during uncoarsening is important to attribute gains to the proposed paradigm. In the revised manuscript, we will add an ablation study comparing the full ComPart framework against a variant that disables community detection only in the uncoarsening phase (while retaining the generalized locally-dense decomposition for initial partitioning, the same coarsening, and other refinements). This will be presented in §5 with quantitative results on the benchmarks to verify the contribution. revision: yes

  2. Referee: [§3, §4] §3 (Framework Description) and §4 (Theoretical Generalization): The central claim that community structures detected by arbitrary methods during uncoarsening reliably improve global quality without introducing bias rests on the assumption that these structures provide structural guidance superior to standard local refinements; however, no quantitative analysis or counterexample analysis is provided to bound the conditions under which this holds.

    Authors: The experimental results across standard benchmarks demonstrate consistent quality improvements, supporting that the detected community structures provide effective guidance in practice. However, we acknowledge the value of explicit analysis to bound the conditions. In the revision, we will expand §3 and §4 with a discussion of the conditions under which the approach is expected to hold, drawing on the properties proven in the generalization of locally-dense decomposition, and include a brief analysis of potential edge cases or limitations based on hypergraph density and community detection method characteristics. revision: yes

Circularity Check

0 steps flagged

No circularity in claimed derivations or generalizations

full rationale

The paper introduces a framework integrating community detection post-coarsening and provides formal proofs for generalizing locally-dense decomposition from graphs to hypergraphs as its first hypergraph application. No equations, predictions, or central claims reduce by construction to fitted parameters, self-definitions, or load-bearing self-citations. The advantages are framed as a new paradigm accommodating external methods, with the derivation chain remaining self-contained against external benchmarks and independent proofs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on the validity of the generalized locally-dense decomposition for hypergraphs and the premise that community structures provide useful global guidance during uncoarsening. No free parameters, invented entities, or explicit axioms are stated in the provided text.

pith-pipeline@v0.9.1-grok · 5794 in / 1245 out tokens · 17638 ms · 2026-06-26T21:50:59.234930+00:00 · methodology

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