← 研踪 PaperTrailarXiv:2610.38708 · v1
Curvature-Guided Sparsification for Graphs with Community Structure
首次提交 2026-10-29 03:06
版本更新 2026-10-29 03:06
雷达观测 2026-10-30 10:22
摘要
Graph sparsification in large-scale networks often requires balancing structural accuracy and computational efficiency. Curvature-based methods, particularly Ollivier-Ricci curvature (ORC), have shown strong performance in identifying inter-community bridges, but their computational cost limits scalability. In this work, we propose a two-stage curvature-guided pipeline for graph sparsification with the goal of preserving community structure. We first use the Lower Ricci Curvature, a combinatorial lower bound of the ORC, to perform efficient edge filtering, substantially reducing graph size before applying the computationally intensive optimal-transport-based ORC curvature and Ricci flow. Results from empirical studies on synthetic and real-world datasets demonstrate that the combined LRC-ORC pipeline can achieve community-recovery accuracy comparable to ORC-only sparsification in terms of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), while reducing runtime on most evaluated benchmarks. Our findings suggest that integrating combinatorial and geometric curvature notions provides a computationally efficient and structurally principled framework for graph sparsification in complex networks.
作者备注：25 pages, 8 figures
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v1 · 2026-10-29 03:06 · 最新观测版本
Curvature-Guided Sparsification for Graphs with Community Structure
Graph sparsification in large-scale networks often requires balancing structural accuracy and computational efficiency. Curvature-based methods, particularly Ollivier-Ricci curvature (ORC), have shown strong performance in identifying inter-community bridges, but their computational cost limits scalability. In this work, we propose a two-stage curvature-guided pipeline for graph sparsification with the goal of preserving community structure. We first use the Lower Ricci Curvature, a combinatorial lower bound of the ORC, to perform efficient edge filtering, substantially reducing graph size before applying the computationally intensive optimal-transport-based ORC curvature and Ricci flow. Results from empirical studies on synthetic and real-world datasets demonstrate that the combined LRC-ORC pipeline can achieve community-recovery accuracy comparable to ORC-only sparsification in terms of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), while reducing runtime on most evaluated benchmarks. Our findings suggest that integrating combinatorial and geometric curvature notions provides a computationally efficient and structurally principled framework for graph sparsification in complex networks.
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