---
title: "RESHPart: Feedback-Reshaped Spectral Embedding for Hypergraph Partitioning"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05729v1"
permalink: "https://feed.astralor.com/items/itm_34a0430f26971229baff1c45"
published: "2026-11-03T23:00:57.000Z"
score: 57
channel: "tech"
tags: ["RAG","RESHPart","Feedback-Reshaped Spectral Embedding","Hypergraph Partitioning With","Laplacian","With","Through"]
---

# RESHPart: Feedback-Reshaped Spectral Embedding for Hypergraph Partitioning

## At a glance

With scalable Laplacian solvers now available, the next frontier is to exploit them more effectively for high-quality hypergraph partitioning. In this work, we revisit spectral hypergraph partitioning from the perspecti…

## Key points

- With scalable Laplacian solvers now available, the next frontier is to exploit them more effectively for high-quality hypergraph partitioning.
- In this work, we revisit spectral hypergraph partitioning from the perspective of spectral embedding geometry.
- Through a systematic analysis of circuit hypergraphs and controlled synthetic (hyper)graphs, we identify a ray-like embedding structure that helps explain the effectiveness of projection-based partitioning.

## Full text

With scalable Laplacian solvers now available, the next frontier is to exploit them more effectively for high-quality hypergraph partitioning. In this work, we revisit spectral hypergraph partitioning from the perspective of spectral embedding geometry. Through a systematic analysis of circuit hypergraphs and controlled synthetic (hyper)graphs, we identify a ray-like embedding structure that helps explain the effectiveness of projection-based partitioning. We further show that the geometry of spectral embeddings can be systematically influenced through Laplacian design and hyperedge reweighting. Guided by these insights, we propose FRESHPart, a feedback-reshaped spectral partitioning framework that refines the spectral embedding through Geometry Feedback and Search Feedback, producing embeddings that are better aligned with projection-based partitioning. On the Titan23 benchmark, FRESHPart achieves average cut reductions over hMETIS of 13.09% at $ε=2\%$ and 25.21% at $ε=20\%$, surpassing the strongest previously reported results (12.16% and 22.53%, respectively), while completing every benchmark in under 120 s on a single GPU.

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