Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
This addresses the challenge of high representation complexity in graphs for applications like classification and regression, though it is incremental as it builds on existing VAE methods.
The paper tackles the problem of graph-level unsupervised learning by proposing a permutation-invariant variational autoencoder that learns to match node orderings without imposing constraints, achieving effectiveness in reconstruction, generation, and downstream tasks.
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 on various graph reconstruction and generation tasks and evaluate the expressive power of extracted representations for downstream graph-level classification and regression.