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Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.
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Passalis, N. and Tefas, A · 2018
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Graph Attention Networks
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Supervised contrastive learning
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Adversarial graph augmentation to improve graph contrastive learning
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Mixup for node and graph classification
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Graph contrastive learning automated
You, Y., Chen, T., Shen, Y., and Wang, Z · 2021
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Deep wasserstein graph discriminant learning for graph classification
Zhang, T., Wang, Y., Cui, Z., Zhou, C., Cui, B., Huang, H., and Yang, J · 2021
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Li, J., Zhou, P., Xiong, C., and Hoi, S. C · 2020
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Learning to hash with graph neural networks for recommender systems
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Group-aware label transfer for domain adaptive person re-identification
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Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
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He, T., Shen, L., Guo, Y., Ding, G., and Guo, Z · 2022
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Category contrast for unsupervised domain adaptation in visual tasks
Huang, J., Guan, D., Xiao, A., Lu, S., and Shao, L · 2022
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Glcc: A general framework for graph-level clustering
Ju, W., Gu, Y., Chen, B., Sun, G., Qin, Y., Liu, X., Luo, X., and Zhang, M · 2022
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Robust optimization as data augmentation for large-scale graphs
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Amplitude spectrum transformation for open compound domain adaptive semantic segmentation
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Cross-domain contrastive learning for unsupervised domain adaptation
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Dual space graph contrastive learning
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Generic structure extraction with bi-level optimization for graph structure learning
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Sa-gda: Spectral augmentation for graph domain adaptation
Pang, J., Wang, Z., Tang, J., Xiao, M., and Yin, N · 2023
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A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges
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