Title: Deep Graph Infomax,
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.