  Subgraphs are rich substructures in graphs, and their nodes and edges can be
partially observed in real-world tasks. Under partial observation, existing
node- or subgraph-level message-passing produces suboptimal representations. In
this paper, we formulate a novel task of learning representations of partially
observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax
(PSI) framework and generalize existing InfoMax models, including DGI,
InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the
mutual information between the partial subgraph's summary and various
substructures from nodes to full subgraphs. In addition, we suggest a novel
two-stage model with $k$-hop PSI, which reconstructs the representation of the
full subgraph and improves its expressiveness from different local-global
structures. Under training and evaluation protocols designed for this problem,
we conduct experiments on three real-world datasets and demonstrate that PSI
models outperform baselines.

Machine Learning;Artificial Intelligence;Social and Information Networks