[2603.26178] Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow

Computer Science > Machine Learning arXiv:2603.26178 (cs) [Submitted on 27 Mar 2026] Title:Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow Authors:Jicheng Ma, Yunyan Yang, Juan Zhao, Liang Zhao View a PDF of the paper titled Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow, by Jicheng Ma and Yunyan Yang and Juan Zhao and Liang Zhao View PDF HTML (experimental) Abstract:We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning by modeling geometric evolution on graphs. Specifically, GEGCN employs a Long Short-Term Memory to model the structural sequence generated by discrete Ricci flow, and the learned dynamic representations are infused into a Graph Convolutional Network. Extensive experiments demonstrate that GEGCN achieves state-of-the-art performance on classification tasks across various benchmark datasets, with its performance being particularly outstanding on heterophilic graphs. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.26178 [cs.LG]   (or arXiv:2603.26178v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.26178 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Liang Zhao [view email] [v1] Fri, 27 Mar 2026 08:49:40 UTC (4,447 KB) Full-text links: Access Paper: View a PDF of the paper titled Geometric Evo...