SlowGuess/ACL_2020_OCR huggingface dataset:
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang. 2019. On the accuracy of influence functions for measuring group effects. In Advances in Neural Information Processing Systems, pages 5255-5265.
Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, and Tomoko Ohkuma. 2017. Unifying text, metadata, and user network representations with a neural network for geolocation prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1260-1272.
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 459-467. ACM.
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2015. Twitter user geolocation using a unified text and network prediction model. In Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics, pages 630-636.
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2017. A neural model for user geolocation and lexical dialectology. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 209-216.
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised user geolocation via graph convolutional networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 2009-2019.
Stephen Roller, Michael Speriosu, Sarat Rallapalli, Benjamin Wing, and Jason Baldridge. 2012. Supervised text-based geolocation using language models on an adaptive grid. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 1500-1510.
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiao zhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web, pages 1067-1077.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations.
Benjamin P Wing and Jason Baldridge. 2011. Simple supervised document geolocation with geodesic grids. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 955-964.
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019.
Simplifying graph convolutional networks. In Proceedings of the International Conference on Machine Learning, pages 6861-6871.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations.
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in Neural Information Processing Systems, pages 4805-4815.
Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In Proceedings of the International Conference on Machine Learning, pages 7134-7143.
Xin Zheng, Jialong Han, and Aixin Sun. 2018. A survey of location prediction on twitter. IEEE Transactions on Knowledge and Data Engineering, 30(9):1652-1671.
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. Computing Research Repository, abs/1812.08434. Version 4.
Daniel Züigner and Stephan Gunnemann. 2019a. Adversarial attacks on graph neural networks via meta learning. In Proceedings of the International Conference on Learning Representations.
Daniel Zügner and Stephan Gümnmann. 2019b. Certifiable robustness and robust training for graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 246-256.