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Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task.
Protein function prediction via graph kernels
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Scalable graph neural networks via bidirectional propagation
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Contrastive Multi-View Representation Learning on Graphs. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research)
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Open Graph Benchmark: Datasets for Machine Learning on Graphs. In NeurIPS
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Graph Structure Learning for Robust Graph Neural Networks. In KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020
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TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
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scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
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Localized Graph Collaborative Filtering
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Motif-based graph self-supervised learning for molecular property prediction
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Improving graph neural network expressivity via subgraph isomorphism counting
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Going Deeper into Permutation-Sensitive Graph Neural Networks
Zhongyu Huang, Yingheng Wang, Chaozhuo Li, and Huiguang He. 2022 · 2022
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HousE: Knowledge Graph Embedding with Householder Parameterization
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Improving Relevance Modeling via Heterogeneous Behavior Graph Learning in Bing Ads. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3713–3721
Bochen Pang, Chaozhuo Li, Yuming Liu, Jianxun Lian, Jianan Zhao, Hao Sun, Weiwei Deng, Xing Xie, and Qi Zhang. 2022 · 2022
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Localized Graph Collaborative Filtering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) . SIAM, 540–548
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Graph Neural Networks for Multimodal Single-Cell Data Integration. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4153–4163
Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, and Jiliang Tang. 2022 · 2022
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Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021 . 2069–2080
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021 · 2080
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