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Graph Neural Networks (GNNs) are popular machine learning methods for modeling graph data.
Learning discrete structures for graph neural networks. In Proceedings of International conference on machine learning . 1972–1982
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019 · 1982
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Social structure of Facebook networks
Amanda L Traud, Peter J Mucha, and Mason A Porter. 2012 · 2012
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How to learn a graph from smooth signals. In Proceedings of Artificial Intelligence and Statistics . 920–929
Vassilis Kalofolias. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 1025–1035
William L Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
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Learning sparse neural networks through L _ 0 L\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma. 2017 · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
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Traffic events oriented dynamic traffic assignment model for expressway network: a network flow approach
Lun Du, Guojie Song, Yiming Wang, Jipeng Huang, Mengfei Ruan, and Zhanyuan Yu. 2018 · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
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Adaptive graph convolutional neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence . 3546–3553
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018 · 2018
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Motifnet: a motif-based graph convolutional network for directed graphs. In Proceedings of 2018 IEEE Data Science Workshop (DSW) . 225–228
Federico Monti, Karl Otness, and Michael M Bronstein. 2018 · 2018
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Graph signal processing: Overview, challenges, and applications
Antonio Ortega, Pascal Frossard, Jelena Kovačević, José MF Moura, and Pierre Vandergheynst. 2018 · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In Proceedings of International Conference on Machine :earning . 21–29
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
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Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann. 2019 · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2019b · 2019
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019 · 2019
Cited alongside, same era.
Bayesian graph convolutional neural networks for semi-supervised classification. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 5829–5836
Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Ustebay. 2019 · 2019
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav. 2020 · 2020
Cited alongside, same era.
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) . 954–959
TabularNet: A neural network architecture for understanding semantic structures of tabular data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 322–331
Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, and Dongmei Zhang. 2021 · 2021
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2021 · 2021
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim. 2021 · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein. 2021 · 2021
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Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, and Kunqing Xie. 2020c · 2020
Cited alongside, same era.
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020b · 2020
Cited alongside, same era.
Adaptive Universal Generalized PageRank Graph Neural Network. In Proceedings of International Conference on Learning Representations
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2020 · 2020
Cited alongside, same era.
Exploring structure-adaptive graph learning for robust semi-supervised classification. In Proceedings of 2020 IEEE International Conference on Multimedia and Expo (ICME) . 1–6
Xiang Gao, Wei Hu, and Zongming Guo. 2020 · 2020
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2020
Cited alongside, same era.
Inferring explicit and implicit social ties simultaneously in mobile social networks
Guojie Song, Yuanhao Li, Junshan Wang, and Lun Du. 2020 · 2020
Cited alongside, same era.
Cocogum: Contextual code summarization with multi-relational gnn on umls
Yanlin Wang, Lun Du, Ensheng Shi, Yuxuan Hu, Shi Han, and Dongmei Zhang. 2020 · 2020
Cited alongside, same era.
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b · 2020
Cited alongside, same era.
Graph Sparsification via Meta-Learning
Guihong Wan and Harsha Kokel. 2021 · 2021
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Tao Wang, Rui Wang, Di Jin, Dongxiao He, and Yuxiao Huang. 2021 · 2021
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. 2021 · 2021
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Domain adaptive classification on heterogeneous information networks. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence . 1410–1416
Shuwen Yang, Guojie Song, Yilun Jin, and Lun Du. 2021 · 2021
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Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
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MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution
Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, and Dongmei Zhang. 2022 · 2022
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GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily. In Proceedings of the ACM Web Conference 2022 . 1550–1558
Lun Du, Xiaozhou Shi, Qiang Fu, Xiaojun Ma, Hengyu Liu, Shi Han, and Dongmei Zhang. 2022b · 2022
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Differentiable graph module (dgm) for graph convolutional networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael Bronstein. 2022 · 2022
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TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2275–2285
Di Yao, Haonan Hu, Lun Du, Gao Cong, Shi Han, and Jingping Bi. 2022 · 2022
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A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. 2022 · 2022
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