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Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","volume-title":"International Conference on Learning Representations.","author":"Klicpera Johannes","year":"2018","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2018 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In International Conference on Learning Representations. Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553447"},{"key":"e_1_3_2_2_30_1","volume-title":"Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations. arXiv preprint arXiv:2102.04064","author":"Leng Dawei","year":"2021","unstructured":"Dawei Leng , Jinjiang Guo , Lurong Pan , Jie Li , and Xinyu Wang . 2021. Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations. arXiv preprint arXiv:2102.04064 ( 2021 ). Dawei Leng, Jinjiang Guo, Lurong Pan, Jie Li, and Xinyu Wang. 2021. Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations. arXiv preprint arXiv:2102.04064 (2021)."},{"key":"e_1_3_2_2_31_1","volume-title":"Can GCNs Go as Deep as CNNs? CoRR abs\/1904.03751","author":"Li Guohao","year":"2019","unstructured":"Guohao Li , Matthias M\u00fcller , Ali K. Thabet , and Bernard Ghanem . 2019. 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Bgl: Gpu-efficient gnn training by optimizing graph data i\/o and preprocessing. arXiv preprint arXiv:2112.08541 (2021)."},{"key":"e_1_3_2_2_34_1","unstructured":"Xiaoze Liu JunyangWu Tianyi Li Lu Chen and Yunjun Gao. 2023. Unsupervised Entity Alignment for Temporal Knowledge Graphs. In WWW.  Xiaoze Liu JunyangWu Tianyi Li Lu Chen and Yunjun Gao. 2023. Unsupervised Entity Alignment for Temporal Knowledge Graphs. In WWW."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272010"},{"key":"e_1_3_2_2_36_1","volume-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Ma Lingxiao","year":"2019","unstructured":"Lingxiao Ma , Zhi Yang , Youshan Miao , Jilong Xue , Ming Wu , Lidong Zhou , and Yafei Dai . 2019 . {NeuGraph}: Parallel Deep Neural Network Computation on Large Graphs . In 2019 USENIX Annual Technical Conference (USENIX ATC 19) . 443--458. Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2019. {NeuGraph}: Parallel Deep Neural Network Computation on Large Graphs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). 443--458."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533367"},{"key":"e_1_3_2_2_38_1","volume-title":"Pytorch-direct: Enabling gpu centric data access for very large graph neural network training with irregular accesses. arXiv preprint arXiv:2101.07956","author":"Min Seung Won","year":"2021","unstructured":"Seung Won Min , Kun Wu , Sitao Huang , Mert Hidayeto\u011flu , Jinjun Xiong , Eiman Ebrahimi , Deming Chen , and Wen-mei Hwu. 2021 . Pytorch-direct: Enabling gpu centric data access for very large graph neural network training with irregular accesses. arXiv preprint arXiv:2101.07956 (2021). Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayeto\u011flu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, and Wen-mei Hwu. 2021. Pytorch-direct: Enabling gpu centric data access for very large graph neural network training with irregular accesses. arXiv preprint arXiv:2101.07956 (2021)."},{"key":"e_1_3_2_2_39_1","volume-title":"Graph classification and clustering based on vector space embedding","author":"Riesen Kaspar","unstructured":"Kaspar Riesen and Horst Bunke . 2010. Graph classification and clustering based on vector space embedding . Vol. 77 . World Scientific . Kaspar Riesen and Horst Bunke. 2010. Graph classification and clustering based on vector space embedding. Vol. 77. World Scientific."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38527-8_16"},{"key":"e_1_3_2_2_41_1","volume-title":"European semantic web conference","author":"Schlichtkrull Michael","unstructured":"Michael Schlichtkrull , Thomas N Kipf , Peter Bloem , Rianne van den Berg , Ivan Titov , and MaxWelling. 2018. Modeling relational data with graph convolutional networks . In European semantic web conference . Springer , 593--607. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and MaxWelling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593--607."},{"key":"e_1_3_2_2_42_1","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Thorpe John","year":"2021","unstructured":"John Thorpe , Yifan Qiao , Jonathan Eyolfson , Shen Teng , Guanzhou Hu , Zhihao Jia , Jinliang Wei , Keval Vora , Ravi Netravali , Miryung Kim , 2021 . Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads . In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) . 495--514. John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, et al. 2021. Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). 495--514."},{"key":"e_1_3_2_2_43_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. 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Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. 2020. GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs. arXiv preprint arXiv:2006.06608 (2020)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/142"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915220"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512185"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456247"},{"key":"e_1_3_2_2_51_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 . 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Advances in neural information processing systems 31 ( 2018 ). Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514069"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_3_2_2_63_1","volume-title":"Accelerating large scale real-time GNN inference using channel pruning. arXiv preprint arXiv:2105.04528","author":"Zhou Hongkuan","year":"2021","unstructured":"Hongkuan Zhou , Ajitesh Srivastava , Hanqing Zeng , Rajgopal Kannan , and Viktor Prasanna . 2021. Accelerating large scale real-time GNN inference using channel pruning. arXiv preprint arXiv:2105.04528 ( 2021 ). Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, and Viktor Prasanna. 2021. 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