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Bell system technical journal 49, 2","author":"Kernighan Brian W","year":"1970","unstructured":"Brian W Kernighan and Shen Lin . 1970. An efficient heuristic procedure for partitioning graphs. Bell system technical journal 49, 2 ( 1970 ), 291--307. Brian W Kernighan and Shen Lin. 1970. An efficient heuristic procedure for partitioning graphs. Bell system technical journal 49, 2 (1970), 291--307."},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . In International Conference on Learning Representations (ICLR). Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_23_1","unstructured":"KONECT. 2017. 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SciPy: Sparse Matrix CSC format. http:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.sparse.csc_matrix.html. accessed May-2020."},{"key":"e_1_3_2_2_43_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine 29, 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_44_1","volume-title":"Reducing Communication in Graph Neural Network Training. arXiv preprint arXiv:2005.03300","author":"Tripathy Alok","year":"2020","unstructured":"Alok Tripathy , Katherine Yelick , and Aydin Buluc . 2020. 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In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/3055540.3055543"},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of the ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang , Lingfan Yu , Da Zheng , Quan Gan , Yu Gai , Zihao Ye , Mufei Li , Jinjing Zhou , Qi Huang , Chao Ma , Ziyue Huang , Qipeng Guo , Hao Zhang , Haibin Lin , Junbo Zhao , Jinyang Li , Alexander J Smola , and Zheng Zhang . 2019 . Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs . In Proceedings of the ICLR Workshop on Representation Learning on Graphs and Manifolds. Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang. 2019. 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