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arXiv · 2603.26178

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

Jicheng Ma·Yunyan Yang·Juan Zhao·Liang Zhao
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Abstract

We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning through explicit modeling of geometric evolution on graph structures. Specifically, GEGCN leverages a Long Short-Term Memory (LSTM) network to capture the dynamic structural sequence generated by discrete Ricci flow, and infuses the learned dynamic representations into a graph convolutional network. Extensive experiments demonstrate that GEGCN achieves excellent performance on classification tasks across various benchmark datasets, including homophilic/heterophilic graphs, filtered graphs, and large-scale graphs.

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Jicheng Ma, Yunyan Yang, Juan Zhao, Liang Zhao. 2026-03-27. Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow. https://arxiv.org/abs/2603.26178

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