Multiresolution Graph Variational Autoencoder
- URL: http://arxiv.org/abs/2106.00967v1
- Date: Wed, 2 Jun 2021 06:28:47 GMT
- Title: Multiresolution Graph Variational Autoencoder
- Authors: Truong Son Hy and Risi Kondor
- Abstract summary: We propose Multiresolution Graph Networks (MGN) and Multiresolution Graph Variational Autoencoders (MGVAE)
At each resolution level, MGN employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution.
MGVAE constructs a hierarchical generative model based on MGN to variationally autoencode the hierarchy of coarsened graphs.
- Score: 11.256959274636724
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose Multiresolution Graph Networks (MGN) and
Multiresolution Graph Variational Autoencoders (MGVAE) to learn and generate
graphs in a multiresolution and equivariant manner. At each resolution level,
MGN employs higher order message passing to encode the graph while learning to
partition it into mutually exclusive clusters and coarsening into a lower
resolution. MGVAE constructs a hierarchical generative model based on MGN to
variationally autoencode the hierarchy of coarsened graphs. Our proposed
framework is end-to-end permutation equivariant with respect to node ordering.
Our methods have been successful with several generative tasks including link
prediction on citation graphs, unsupervised molecular representation learning
to predict molecular properties, molecular generation, general graph generation
and graph-based image generation.
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