  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. graph2vec) but they tend to only consider certain substructures
(e.g. subtrees) as graph representatives. Inspired by recent progress of
unsupervised representation learning, in this paper we proposed a novel method
called InfoGraph for learning graph-level representations. We maximize the
mutual information between the graph-level representation and the
representations of substructures of different scales (e.g., nodes, edges,
triangles). By doing so, the graph-level representations encode aspects of the
data that are shared across different scales of substructures. Furthermore, we
further propose InfoGraph*, an extension of InfoGraph for semi-supervised
scenarios. InfoGraph* maximizes the mutual information between unsupervised
graph representations learned by InfoGraph and the representations learned by
existing supervised methods. As a result, the supervised encoder learns from
unlabeled data while preserving the latent semantic space favored by the
current supervised task. Experimental results on the tasks of graph
classification and molecular property prediction show that InfoGraph is
superior to state-of-the-art baselines and InfoGraph* can achieve performance
competitive with state-of-the-art semi-supervised models.
