Pre-training on large-scale heterogeneous graph
Graph neural networks (GNNs) emerge as the state-of-the-art representation learning methods on graphs and often rely on a large amount of labeled data to achieve satisfactory performance. Recently, in order to relieve the label scarcity issues, some works propose to pre-train GNNs in a self-supervis...
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| Main Authors: | , , , , , |
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| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2021
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| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/6888 https://ink.library.smu.edu.sg/context/sis_research/article/7891/viewcontent/KDD21_PT_HGNN.pdf |
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| 總結: | Graph neural networks (GNNs) emerge as the state-of-the-art representation learning methods on graphs and often rely on a large amount of labeled data to achieve satisfactory performance. Recently, in order to relieve the label scarcity issues, some works propose to pre-train GNNs in a self-supervised manner by distilling transferable knowledge from the unlabeled graph structures. Unfortunately, these pre-training frameworks mainly target at homogeneous graphs, while real interaction systems usually constitute large-scale heterogeneous graphs, containing different types of nodes and edges, which leads to new challenges on structure heterogeneity and scalability for graph pre-training. In this paper, we first study the problem of pre-training on large-scale heterogeneous graph and propose a novel pre-training GNN framework, named PT-HGNN. The proposed PT-HGNN designs both the node- and schema-level pre-training tasks to contrastively preserve heterogeneous semantic and structural properties as a form of transferable knowledge for various downstream tasks. In addition, a relationbased personalized PageRank is proposed to sparsify large-scale heterogeneous graph for efficient pre-training. Extensive experiments on one of the largest public heterogeneous graphs (OAG) demonstrate that our PT-HGNN significantly outperforms various state-of-the-art baselines. |
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