Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
This work addresses graph representation learning for tasks like classification, but it appears incremental as it builds on existing methods like Graph Convolutional Networks and Ricci flow.
The paper tackles the problem of enhancing graph representation learning by modeling geometric evolution on graphs, achieving state-of-the-art performance on classification tasks across various benchmark datasets, with particularly outstanding results on heterophilic graphs.
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.