SAGES: Scalable Attributed Graph Embedding with Sampling for Unsupervised Learning

Abstract

Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling. However, most unsupervised graph representation learning methods suffer issues like poor scalability or limited utilization of content/structural relationships, especially on attributed graphs. In this paper, we propose SAGES, a graph sampling based autoencoder framework, which can alleviate these issues. Specifically, we propose a graph sampler considering both structural and content features, in which nodes with greater influence on each other have more chances to be sampled in the same subgraph. In addition, an unbiased Graph Autoencoder (GAE) with structure-level, content-level, and community-level reconstruction loss is built from the properly sampled subgraph each iteration. The time and space complexity analysis is carried out to show the scalability of SAGES. We conducted experiments on three medium-size attributed graphs and three large attributed graphs. Experimental results illustrate that SAGES achieves the competitive performance in unsupervised attributed graph learning on various downstream tasks including node classification, link prediction, and node clustering.