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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.26178 |
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| _version_ | 1866911651455827968 |
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| author | Ma, Jicheng Yang, Yunyan Zhao, Juan Zhao, Liang |
| author_facet | Ma, Jicheng Yang, Yunyan Zhao, Juan Zhao, Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow Ma, Jicheng Yang, Yunyan Zhao, Juan Zhao, Liang Machine Learning 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. |
| title | Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.26178 |