ELLIS header
University of Stuttgart Logo
Max Planck Institute for Intelligent Systems Logo

Learning Graph Representations with Embedding Propagation

Alberto García-Durán, Mathias Niepert

Advances in Neural Information Processing Systems (NeurIPS), pp. 5125–5136, 2017.


Abstract

We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes. Backward messages consist of gradients that result from aggregating the label representations and applying a reconstruction loss. Node representations are finally computed from the representation of their labels. With significantly fewer parameters and hyperparameters an instance of EP is competitive with and often outperforms state of the art unsupervised and semi-supervised learning methods on a range of benchmark data sets.

Links


BibTeX

@inproceedings{garciaduran17_neurips, title = {Learning Graph Representations with Embedding Propagation}, author = {García-Durán, Alberto and Niepert, Mathias}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, pages = {5125–5136}, year = {2017}, url = {https://proceedings.neurips.cc/paper/2017/file/e0688d13958a19e087e123148555e4b4-Paper.pdf}, doi = {10.5555/3295222.3295265}, preprint = {https://arxiv.org/abs/1710.03059} }