Graph Pooling via Ricci Flow

TL;DR

This paper introduces ORC-Pool, a novel graph pooling operator that leverages Ricci curvature and geometric flow to improve clustering in attributed graphs, enhancing graph neural network performance.

Contribution

The paper presents a new graph pooling method that incorporates Ollivier's Ricci curvature to handle attributed graphs, extending Ricci flow clustering to machine learning applications.

Findings

Effective clustering of attributed graphs using Ricci curvature.

Improved GNN performance with the proposed pooling layer.

Integration of geometric coarsening into neural network architectures.

Abstract

Graph Machine Learning often involves the clustering of nodes based on similarity structure encoded in the graph's topology and the nodes' attributes. On homophilous graphs, the integration of pooling layers has been shown to enhance the performance of Graph Neural Networks by accounting for inherent multi-scale structure. Here, similar nodes are grouped together to coarsen the graph and reduce the input size in subsequent layers in deeper architectures. In both settings, the underlying clustering approach can be implemented via graph pooling operators, which often rely on classical tools from Graph Theory. In this work, we introduce a graph pooling operator (ORC-Pool), which utilizes a characterization of the graph's geometry via Ollivier's discrete Ricci curvature and an associated geometric flow. Previous Ricci flow based clustering approaches have shown great promise across several domains, but are by construction unable to account for similarity structure encoded in the node attributes. However, in many ML applications, such information is vital for downstream tasks. ORC-Pool extends such clustering approaches to attributed graphs, allowing for the integration of geometric coarsening into Graph Neural Networks as a pooling layer.