Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

@inproceedings{Bojchevski2017DeepGE,
  title={Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking},
  author={Aleksandar Bojchevski and Stephan Gunnemann},
  year={2017}
}
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss – an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian… CONTINUE READING
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