Timezone: »

Learning Graph Representations with Embedding Propagation
Alberto Garcia Duran · Mathias Niepert

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #18 #None

We propose EP, Embedding Propagation, 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.

Author Information

Alberto Garcia Duran (NEC Europe)
Mathias Niepert (NEC Labs Europe)

More from the Same Authors