Keywords: Unsupervised Learning, Graph Neural Networks, Graph Convolutions, Mutual Information, Infomax, Deep Learning
TL;DR: A new method for unsupervised representation learning on graphs, relying on maximizing mutual information between local and global representations in a graph. State-of-the-art results, competitive with supervised learning.
Abstract: We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.
Code: [![github](/images/github_icon.svg) PetarV-/DGI](https://github.com/PetarV-/DGI) + [![Papers with Code](/images/pwc_icon.svg) 10 community implementations](https://paperswithcode.com/paper/?openreview=rklz9iAcKQ)
Data: [Citeseer](https://paperswithcode.com/dataset/citeseer), [Cora](https://paperswithcode.com/dataset/cora), [PPI](https://paperswithcode.com/dataset/ppi), [Pubmed](https://paperswithcode.com/dataset/pubmed), [Reddit](https://paperswithcode.com/dataset/reddit)