用里奇流模拟图结构演化，提升图表示学习效果
Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
- 用LSTM捕捉离散里奇流生成的动态结构序列
- 在多种图数据上分类性能显著优于基线方法
- 适合处理异质、大规模及过滤后图数据
我们提出几何演化图卷积网络（GEGCN），通过显式建模图结构的几何演化来增强图表示学习。具体而言，GEGCN利用长短期记忆网络（LSTM）捕捉由离散里奇流生成的动态结构序列，并将学习到的动态表征注入图卷积网络。大量实验表明，GEGCN在多种基准数据集上的分类任务中表现优异，涵盖同质图、异质图、过滤图以及大规模图。
原文摘要 · Abstract (English)
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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