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.

翻译：在部分观察下,现有的节点或子节点级信件传递分层结构产生亚最佳的表示方式。在本文中,我们制定了学习部分观测的分层结构的新任务。为了解决这个问题,我们提出部分子分层信息(PSI)框架,并将现有的信息数据模型(包括DGI、Infograph、MVGRL和GreaphCL)纳入我们的框架。这些模型最大限度地扩大了部分子集摘要和从节点到完整子集的各种分层之间的相互信息。此外,我们建议用美元-hop PSI建立一个两阶段的新模式,以重建整个分层的表示方式,并从不同的地方-全球结构中改进它的表达性。根据为此设计的培训和评价协议,我们在三个真实世界的数据集上进行实验,并证明PSI模型超越了基准。