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Surv."],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. 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Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_3_89_2","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903.","journal-title":"arXiv preprint arXiv:1710.10903"},{"key":"e_1_3_3_90_2","first-page":"88","volume-title":"Advances in Neural Information Processing Systems 30","author":"Verma Saurabh","year":"2017","unstructured":"Saurabh Verma and Zhi-Li Zhang. 2017. Hunt for the unique, stable, sparse and fast feature learning on graphs. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 88\u201398. Retrieved from http:\/\/papers.nips.cc\/paper\/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf."},{"key":"e_1_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTON51198.2020.9203477"},{"key":"e_1_3_3_92_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_3_93_2","article-title":"Deep graph library: Towards efficient and scalable deep learning on graphs","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. Retrieved from https:\/\/arxiv.org\/abs\/1909.01315.","journal-title":"Proceedings of the ICLR Workshop on Representation Learning on Graphs and Manifolds"},{"key":"e_1_3_3_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_3_96_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"e_1_3_3_97_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ddtec.2020.11.009"},{"key":"e_1_3_3_98_2","article-title":"Simplifying graph convolutional networks","volume":"1902","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Tianyi Zhang, Amauri H. Souza Jr., Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying graph convolutional networks. 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