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Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
[article]
2021
arXiv
pre-print
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
arXiv:2104.09856v2
fatcat:dcmxbft6hrbxpmjpkn4fdovzfu