Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

arXiv: Learning, 2019.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of labeled data and expressive features, which might be inaccessible for some applications. To tackle this problem, we propose a pre-training framework that captures ...More

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