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Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs.
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How powerful are graph neural networks?
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Are powerful graph neural nets necessary? a dissection on graph classification
Chen, T., Bian, S., and Sun, Y · 2019
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Strategies for pre-training graph neural networks
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Iterative graph self-distillation
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Hop-count based self-supervised anomaly detection on attributed networks
Huang, T., Pei, Y., Menkovski, V., and Pechenizkiy, M · 2021
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Graph self-supervised learning: A survey
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Node embedding using mutual information and self-supervision based bi-level aggregation
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Learning graph representation by aggregating subgraphs via mutual information maximization
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Self-supervised learning of graph neural networks: A unified review
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