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Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_31_1","volume-title":"Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997","author":"Klicpera Johannes","year":"2018","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 ( 2018 ). Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)."},{"key":"e_1_3_2_2_32_1","volume-title":"Diffusion improves graph learning. arXiv preprint arXiv:1911.05485","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera , Stefan Wei\u00dfenberger , and Stephan G\u00fcnnemann . 2019. 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Transformer for graphs: An overview from architecture perspective. arXiv preprint arXiv:2202.08455 (2022)."},{"key":"e_1_3_2_2_47_1","volume-title":"Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947","author":"Oono Kenta","year":"2019","unstructured":"Kenta Oono and Taiji Suzuki . 2019. Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947 ( 2019 ). Kenta Oono and Taiji Suzuki. 2019. Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947 (2019)."},{"key":"e_1_3_2_2_49_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. 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Equivariant and stable positional encoding for more powerful graph neural networks. arXiv preprint arXiv:2203.00199 ( 2022 ). Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li. 2022. Equivariant and stable positional encoding for more powerful graph neural networks. arXiv preprint arXiv:2203.00199 (2022)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_66_1","DOI":"10.1145\/3394486.3403177"},{"key":"e_1_3_2_2_67_1","first-page":"24509","article-title":"AutoGEL: An Automated Graph Neural Network with Explicit Link Information","volume":"34","author":"Wang Zhili","year":"2021","unstructured":"Zhili Wang , Shimin Di , and Lei Chen . 2021 . AutoGEL: An Automated Graph Neural Network with Explicit Link Information . Advances in Neural Information Processing Systems 34 (2021), 24509 -- 24522 . Zhili Wang, Shimin Di, and Lei Chen. 2021. AutoGEL: An Automated Graph Neural Network with Explicit Link Information. 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How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_2_73_1","volume-title":"International conference on machine learning. PMLR, 5453--5462","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Chengtao Li , Yonglong Tian , Tomohiro Sonobe , Ken-ichi Kawarabayashi, and Stefanie Jegelka . 2018 . Representation learning on graphs with jumping knowledge networks . In International conference on machine learning. PMLR, 5453--5462 . Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In International conference on machine learning. PMLR, 5453--5462."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_74_1","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_2_75_1","volume-title":"Hierarchical graph representation learning with differentiable pooling. 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Advances in neural information processing systems 31 (2018)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_77_1","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"e_1_3_2_2_78_1","volume-title":"Gmlp: Building scalable and flexible graph neural networks with feature-message passing. arXiv preprint arXiv:2104.09880","author":"Zhang Wentao","year":"2021","unstructured":"Wentao Zhang , Yu Shen , Zheyu Lin , Yang Li , Xiaosen Li , Wen Ouyang , Yangyu Tao , Zhi Yang , and Bin Cui . 2021 . Gmlp: Building scalable and flexible graph neural networks with feature-message passing. arXiv preprint arXiv:2104.09880 (2021). Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. 2021. Gmlp: Building scalable and flexible graph neural networks with feature-message passing. arXiv preprint arXiv:2104.09880 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_79_1","DOI":"10.1145\/3485447.3511986"},{"key":"e_1_3_2_2_80_1","first-page":"20321","article-title":"Node dependent local smoothing for scalable graph learning","volume":"34","author":"Zhang Wentao","year":"2021","unstructured":"Wentao Zhang , Mingyu Yang , Zeang Sheng , Yang Li , Wen Ouyang , Yangyu Tao , Zhi Yang , and Bin Cui . 2021 . Node dependent local smoothing for scalable graph learning . Advances in Neural Information Processing Systems 34 (2021), 20321 -- 20332 . Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. 2021. Node dependent local smoothing for scalable graph learning. Advances in Neural Information Processing Systems 34 (2021), 20321--20332.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_81_1","volume-title":"Automated machine learning on graphs: A survey. arXiv preprint arXiv:2103.00742","author":"Zhang Ziwei","year":"2021","unstructured":"Ziwei Zhang , Xin Wang , and Wenwu Zhu . 2021. Automated machine learning on graphs: A survey. arXiv preprint arXiv:2103.00742 ( 2021 ). Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2021. Automated machine learning on graphs: A survey. arXiv preprint arXiv:2103.00742 (2021)."},{"key":"e_1_3_2_2_82_1","volume-title":"International Conference on Learning Representations.","author":"Zhu Hao","year":"2020","unstructured":"Hao Zhu and Piotr Koniusz . 2020 . Simple spectral graph convolution . In International Conference on Learning Representations. Hao Zhu and Piotr Koniusz. 2020. Simple spectral graph convolution. 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