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A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

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arxiv 2202.07893 v2 pith:4HEBFXO7 submitted 2022-02-16 cs.LG cs.SIq-bio.BM

Jun Xia , Yanqiao Zhu , Yuanqi Du , Stan Z. Li This is my paper · ORCID
classification cs.LGcs.SIq-bio.BM
keywords pgmsgraphapplicationsgraphsknowledgelanguagelearningmodel
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Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to Pretrained Graph Models (PGMs). Owing to the powerful model architectures of PGMs, abundant knowledge from massive labeled and unlabeled graph data can be captured. The knowledge implicitly encoded in model parameters can benefit various downstream tasks and help to alleviate several fundamental issues of learning on graphs. In this paper, we provide the first comprehensive survey for PGMs. We firstly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Then, we systematically categorize existing PGMs based on a taxonomy from four different perspectives. Next, we present the applications of PGMs in social recommendation and drug discovery. Finally, we outline several promising research directions that can serve as a guideline for future research.

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Cited by 2 Pith papers

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  1. GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning

    cs.CR 2024-11 conditional novelty 6.0 of 10

    An empirical study showing that graph prompt learning exposes node attributes and links to inference attacks, with prompt tuning adding little extra risk over frozen GNN baselines.

  2. Heterogeneous Graph Pre-training Based Model for Secure and Efficient Prediction of Default Risk Propagation among Bond Issuers

    cs.LG 2025-01 conditional novelty 4.0 of 10

    A heterogeneous-graph masked autoencoder (HGMAE) pre-training method yields a 0.831 Micro-F1 for bond default risk propagation prediction, 0.006 higher than GraphMAE, on a self-built 20M-node enterprise graph.

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