Gromov-Wasserstein Factorization Models for Graph Clustering

Understand

We propose a new nonlinear factorization model for graphs that are with topological structures, and optionally, node attributes.

• This model is based on a pseudometric called Gromov-Wasserstein (GW) discrepancy, which compares graphs in a relational way.
• It estimates observed graphs as GW barycenters constructed by a set of atoms with different weights.
• By minimizing the GW discrepancy between each observed graph and its GW barycenter-based estimation, we learn the atoms and their weights associated with the observed graphs.