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

arXiv 2603.26178 ↗First posted 27 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

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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References · 35

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23 more
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