What question did this study set out to answer?

To alleviate model overfitting in graph classification tasks when human-annotated graph labels are scarce by utilizing contrastive self-supervised learning frameworks.

May 18, 2021Open Access

Contrastive Self-supervised Learning for Graph Classification

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To alleviate model overfitting in graph classification tasks when human-annotated graph labels are scarce by utilizing contrastive self-supervised learning frameworks.
Developed a two-stage pretraining and fine-tuning framework alongside a joint contrastive self-supervised learning (CSSL) regularizer for simultaneous supervised and unsupervised optimization.
Generated augmented graph views via sequential structural alteration operations and applied a contrastive loss to determine whether augmented graphs originated from the same parent graph.
Evaluated the proposed CSSL algorithms on multiple standard graph classification benchmark datasets.
Demonstrated qualitative reductions in model overfitting when training on datasets with limited labeled graph instances.
Achieved improved classification performance across diverse graph benchmark datasets relative to standard supervised training baselines.

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Implication

Computational study demonstrates contrastive self-supervised learning mitigates overfitting in graph classification, suggesting effective representation learning without extensive labels.

Cite This Study

Zeng et al. (2021) studied this question.

synapsesocial.com/papers/6a08f62074a93f402dd38d11 https://doi.org/10.1609/aaai.v35i12.17293

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Implication

Computational study demonstrates contrastive self-supervised learning mitigates overfitting in graph classification, suggesting effective representation learning without extensive labels.

Contrastive Self-supervised Learning for Graph Classification

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Discussion

Implication

Cite This Study

Contrastive Self-supervised Learning for Graph Classification

Key Points

Discussion

Implication

Cite This Study

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