Dirichlet Graph Variational Autoencoder
Refereed conference paper presented and published in conference proceedings

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AbstractGraph Neural Networks (GNN) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However there is no clear explanation of what these latent factors are and why they perform well. In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Our study connects VAEs based graph generation and balanced graph cut, and provides a new way to understand and improve the internal mechanism of VAEs based graph generation. Specifically, we first interpret the reconstruction term of DGVAE as balanced graph cut in a principled way. Furthermore, motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships. Heatts utilizes the Taylor series for fast computation of Heat kernels and has better low pass characteristics than Graph Convolutional Networks (GCN). Through experiments on graph generation and graph clustering, we demonstrate the effectiveness of our proposed framework.
Acceptance Date26/09/2020
All Author(s) ListJia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang
Name of Conference34th Conference on Neural Information Processing Systems, NeurIPS 2020
Start Date of Conference06/12/2020
End Date of Conference12/12/2020
Place of ConferenceOnline
Country/Region of ConferenceOthers
Proceedings TitleAdvances in Neural Information Processing Systems
LanguagesEnglish-United Kingdom
Keywordsgraph variational autoencoder

Last updated on 2022-10-01 at 00:29