mlsanityData to 7 Oct 2026

Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

arXiv 2209.00508 ↗First posted 1 Sep 2022cs.LG cs.AI cs.SIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 51 of its 66 references that are in the map. First posted on arXiv 1 Sep 2022.

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51 of 66 references.

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  2. 2024Generalizing Weisfeiler-Lehman Kernels to Subgraphscited by 1