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Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications.
Regression shrinkage and selection via the lasso
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Metapath-Guided Heterogeneous Graph Neural Network for Intent Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 2478–2486
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GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations
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Hardware Acceleration of Large Scale GCN Inference. In 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP) . 61–68
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Graph-Bert: Only Attention is Needed for Learning Graph Representations
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Heterogeneous Graph Neural Networks for Malicious Account Detection. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18) . New York, NY, USA, 2077–2085
Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018 · 2085
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Learning Structured Sparsity in Deep Neural Networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS’16) . Curran Associates Inc., Red Hook, NY, USA, 2082–2090
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016 · 2090
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