Boosting Graph Contrastive Learning via Adaptive Sampling

Sheng Wan

doi:10.1109/TNNLS.2023.3291358

Boosting Graph Contrastive Learning via Adaptive Sampling

2024-11-01

3

PRE

AI

DOI：10.1109/TNNLS.2023.3291358

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Contrastive learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically similar (i.e., positive) and dissimilar (i.e., negative) pairs of examples under different augmented views. Recently, CL has provided unprecedented potential for learning expressive graph representations without external supervision. In graph CL, the negative nodes are typically uniformly sampled from augmented views to formulate the contrastive objective. However, this uniform negative sampling strategy limits the expressive power of contrastive models. To be specific, not all the negative nodes can provide sufficiently meaningful knowledge for effective contrastive representation learning. In addition, the negative nodes that are semantically similar to the anchor are undesirably repelled from it, leading to degraded model performance. To address these limitations, in this article, we devise an adaptive sampling strategy termed AdaS. The proposed AdaS framework can be trained to adaptively encode the importance of different negative nodes, so as to encourage learning from the most informative graph nodes. Meanwhile, an auxiliary polarization regularizer is proposed to suppress the adverse impacts of the false negatives and enhance the discrimination ability of AdaS. The experimental results on a variety of real-world datasets firmly verify the effectiveness of our AdaS in improving the performance of graph CL.

Keyword:

Representation learning

Graph neural networks

Convolution

Perturbation methods

Task analysis

Training

Self-supervised learning

Contrastive graph representation learning

graph convolutional network

negative mining

Boosting Graph Contrastive Learning via Adaptive Sampling

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