InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Understand

This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios.

• Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks.
• Traditional graph kernel based methods are simple, yet effective for obtaining fixed-length representations for graphs but they suffer from poor generalization due to hand-crafted designs.
• There are also some recent methods based on language models (e.g.