# Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning

@inproceedings{Winter2021PermutationInvariantVA, title={Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning}, author={Robin Winter and Frank No'e and Djork-Arn{\'e} Clevert}, booktitle={NeurIPS}, year={2021} }

Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either nodeor 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 representation learning has not received much attention yet. This might be mainly attributed to the high representation complexity of graphs…

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