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Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability.
Sancus: staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks
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Generalization and representational limits of graph neural networks. In International Conference on Machine Learning . PMLR, 3419–3430
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Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
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On the equivalence between positional node embeddings and structural graph representations. In International Conference on Learning Representations
Balasubramaniam Srinivasan and Bruno Ribeiro. 2020 · 2020
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Decoupling the depth and scope of graph neural networks
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Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
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Improving graph neural network expressivity via subgraph isomorphism counting
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6.7 Using GPU for Neighborhood Sampling — DGL 0.9.1post1 documentation
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Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
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Geodesic Graph Neural Network for Efficient Graph Representation Learning
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Accelerating PyG on NVIDIA GPUs
PyG. 2022 · 2022
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Graph neural networks: foundation, frontiers and applications. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4840–4841
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, and Xiaojie Guo. 2022 · 2022
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Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, and Pan Li. 2022 · 2022
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TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, and George Karypis. 2022 · 2022
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Graph Neural Networks for Link Prediction with Subgraph Sketching. In International Conference on Learning Representations
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M Bronstein, and Max Hansmire. 2023 · 2023
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