  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.
