Contrastive Multi-View Representation Learning on Graphs

Code

• github.com/kavehhassani/mvgrl
  OfficialIn paperpytorch★ 275
• github.com/hengruizhang98/mvgrl
  pytorch★ 67
• github.com/timothewt/AdvancedGraphClustering
  none★ 6
• github.com/ycremar/DIG-SSL
  pytorch★ 4
• github.com/dmlc/dgl/tree/master/examples/pytorch/mvgrl
  pytorch★ 0

Abstract

We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by contrasting encodings from first-order neighbors and a graph diffusion. We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol. For example, on Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art. When compared to supervised baselines, our approach outperforms them in 4 out of 8 benchmarks. Source code is released at: https://github.com/kavehhassani/mvgrl

Tasks

Reproductions