Learning Graph Representations with Embedding Propagation

TL;DR

This paper introduces Embedding Propagation, an unsupervised graph learning method that efficiently learns node representations by passing label and gradient messages, outperforming existing methods on benchmarks.

Contribution

It presents a novel message-passing framework for graph embeddings that requires fewer parameters and hyperparameters, achieving competitive results.

Findings

EP outperforms state-of-the-art methods on benchmark datasets.

EP requires fewer parameters and hyperparameters.

EP is effective for both unsupervised and semi-supervised learning.

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.