• DOI:10.1609/AAAI.V34I04.6120
Corpus ID: 208176464

Gromov-Wasserstein Factorization Models for Graph Clustering

@inproceedings{Xu2020GromovWassersteinFM,
  title={Gromov-Wasserstein Factorization Models for Graph Clustering},
  author={H. Xu},
  booktitle={AAAI},
  year={2020}
}

H. Xu

Published in AAAI 2020

Computer Science, Mathematics

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… CONTINUE READING

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