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In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.
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GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction
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Hypergraph Contrastive Collaborative Filtering. In Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval . 70–79
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Learning to Drop: Robust Graph Neural Network via Topological Denoising. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 779–787
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Metropolis-hastings Data Augmentation for Graph Neural Networks
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Fairdrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
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Shuo Yu, Huafei Huang, Minh N. Dao, and Feng Xia. 2022 · 2022
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Behavior-aware Account De-anonymization on Ethereum Interaction Graph
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Temporal Graph Representation Learning with Adaptive Augmentation Contrastive. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 683–699
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Interpolating Graph Pair to Regularize Graph Classification. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 7766–7774
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Spatio-temporal Self-supervised Learning for Traffic Flow Prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 4356–4364
Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, and Yu Zheng. 2023 · 2023
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Calibrating and Improving Graph Contrastive Learning
MA KAILI, Garry YANG, Han Yang, Yongqiang Chen, and James Cheng. 2023 · 2023
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S-Mixup: Structural Mixup for Graph Neural Networks. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 4003–4007
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I’m me, we’re us, and i’m us: Tri-directional Contrastive Learning on Hypergraphs. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 8456–8464
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HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks. In 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 1–10
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A Multi-view Contrastive Learning for Heterogeneous Network Embedding
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MuHca: Mixup Heterogeneous Graphs for Contrastive Learning with Data Augmentation. In Pacific Rim International Conference on Artificial Intelligence . Springer, 377–388
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Graph Mixup with Soft Alignments. In International Conference on Machine Learning . PMLR, 21335–21349
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Learning graph representation by aggregating subgraphs via mutual information maximization
Ziwen Liu, Chenguang Wang, Congying Han, and Tiande Guo. 2023 · 2023
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Hierarchically contrastive hard sample mining for graph self-supervised pretraining
Wenxuan Tu, Sihang Zhou, Xinwang Liu, Chunpeng Ge, Zhiping Cai, and Yue Liu. 2023a · 2023
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Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pretraining
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Data-centric graph learning: A survey
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OMG: Towards Effective Graph Classification against Label Noise
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Automated Spatio-temporal Graph Contrastive Learning. In Proceedings of the ACM Web Conference 2023 . 295–305
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Graph Data Augmentation for Graph Machine Learning: A Survey
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Multi-aspect heterogeneous graph augmentation. In Proceedings of the ACM Web Conference 2023 . 39–48
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A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
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