Permutation-Invariant Variational Autoencoder for Graph-Level Representation LearningDownload PDF

21 May 2021, 20:46 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: variational autoencoder, permutation invariance, graph, self-attention, representation learning, graph autoencoder
  • TL;DR: We propose a variational autoencoder that encodes graphs in a fixed-size latent space that is invariant under permutation of the input graph.
  • Abstract: Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible applications, graph-level unsupervised learning has not received much attention yet. This might be mainly attributed to the high representation complexity of graphs, which can be represented by $n!$ equivalent adjacency matrices, where $n$ is the number of nodes. In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data. Our proposed model indirectly learns to match the node ordering of input and output graph, without imposing a particular node ordering or performing expensive graph matching. We demonstrate the effectiveness of our proposed model for graph reconstruction, generation and interpolation and evaluate the expressive power of extracted representations for downstream graph-level classification and regression.
  • Supplementary Material: pdf
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  • Code: https://github.com/jrwnter/pigvae
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