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Shuiwang Ji | Hao Yuan | Shurui Gui | Yaochen Xie | Bora Oztekin | Youzhi Luo | Keqiang Yan | Yi Liu | Haiyang Yu | Meng Liu | Xuan Zhang | Zhao Xu | Limei Wang | Haoran Liu | Cong Fu | Jingtun Zhang | Shuiwang Ji | Yaochen Xie | Haiyang Yu | Shurui Gui | Zhao Xu | Youzhi Luo | Bora Oztekin | Hao Yuan | Limei Wang | Yi Liu | Meng Liu | Xuan Zhang | Haoran Liu | Keqiang Yan | Cong Fu | Jingtun Zhang | Haonan Yuan
[1] Stephan Günnemann,et al. Directional Message Passing for Molecular Graphs , 2020, ICLR.
[2] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[3] Lingfan Yu,et al. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. , 2019 .
[4] Johannes Klicpera,et al. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules , 2020, ArXiv.
[5] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[6] Alán Aspuru-Guzik,et al. Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models , 2018, Frontiers in Pharmacology.
[7] Weinan Zhang,et al. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation , 2020, ICLR.
[8] Kaveh Hassani,et al. Contrastive Multi-View Representation Learning on Graphs , 2020, ICML.
[9] William L. Hamilton. Graph Representation Learning , 2020, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[10] Jian Tang,et al. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization , 2019, ICLR.
[11] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[12] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[13] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[14] Heiko Hoffmann,et al. Explainability Methods for Graph Convolutional Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] J. Leskovec,et al. Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.
[16] Ruslan Salakhutdinov,et al. Revisiting Semi-Supervised Learning with Graph Embeddings , 2016, ICML.
[17] Klaus-Robert Müller,et al. Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.
[18] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[19] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[20] Qiang Liu,et al. Deep Graph Contrastive Representation Learning , 2020, ArXiv.
[21] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[22] Shuiwang Ji,et al. GraphDF: A Discrete Flow Model for Molecular Graph Generation , 2021, ICML.
[23] Zhangyang Wang,et al. Graph Contrastive Learning with Augmentations , 2020, NeurIPS.
[24] Suhang Wang,et al. Self-supervised Learning on Graphs: Deep Insights and New Direction , 2020, ArXiv.
[25] Shinichi Nakajima,et al. Higher-Order Explanations of Graph Neural Networks via Relevant Walks , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[27] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[28] Jure Leskovec,et al. Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..
[29] Xiaojie Guo,et al. A Systematic Survey on Deep Generative Models for Graph Generation , 2020, ArXiv.
[30] Hamid R. Rabiee,et al. Deep Graph Generators: A Survey , 2020, IEEE Access.
[31] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[32] Defferrard Michaël,et al. Deep Learning on Graphs , 2016 .
[33] Jure Leskovec,et al. GNNExplainer: Generating Explanations for Graph Neural Networks , 2019, NeurIPS.
[34] Shuiwang Ji,et al. Self-Supervised Learning of Graph Neural Networks: A Unified Review , 2021, ArXiv.
[35] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[36] Zibin Zheng,et al. GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software , 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).
[37] Shuiwang Ji,et al. Explainability in Graph Neural Networks: A Taxonomic Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Chang Zhou,et al. CogDL: An Extensive Toolkit for Deep Learning on Graphs , 2021, ArXiv.
[39] Shuiwang Ji,et al. Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery. , 2020, Bioinformatics.
[40] Bo Zong,et al. Parameterized Explainer for Graph Neural Network , 2020, NeurIPS.
[41] Kang Li,et al. On Explainability of Graph Neural Networks via Subgraph Explorations , 2021, International Conference on Machine Learning.
[42] Meng Liu,et al. GraphEBM: Molecular Graph Generation with Energy-Based Models , 2021, ArXiv.
[43] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[44] Emma J. Chory,et al. A Deep Learning Approach to Antibiotic Discovery , 2020, Cell.
[45] Kristian Kersting,et al. TUDataset: A collection of benchmark datasets for learning with graphs , 2020, ArXiv.
[46] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[47] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[48] Shuiwang Ji,et al. XGNN: Towards Model-Level Explanations of Graph Neural Networks , 2020, KDD.
[49] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[50] Jun Hu,et al. Efficient Graph Deep Learning in TensorFlow with tf_geometric , 2021, ACM Multimedia.
[51] Cesare Alippi,et al. Graph Neural Networks in TensorFlow and Keras with Spektral , 2020, IEEE Comput. Intell. Mag..
[52] Shuiwang Ji,et al. Spherical Message Passing for 3D Graph Networks , 2021, ArXiv.
[53] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[54] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.