Many important real-world applications and questions come in the form of graphs, such as social network, protein-protein interaction network, brain network, chemical molecular graph and 3D point cloud. Therefore, driven by interdisciplinary research, the neural network model for graph data-oriented has become an emerging research hotspot. Among them, two of the three pioneers of deep learning, Professor ***Yann LeCun (2018 Turing Award Winner)***, Professor Yoshua Bengio (2018 Turing Award Winner) and famous Professor Jure Leskovec from Stanford University AI lab also participated in it.
This project focuses on GNN, lists relevant must-read papers and keeps track of progress. We look forward to promoting this direction and providing some help to researchers in this direction.
Contributed by Allen Bluce (Bentian Li) and Anne Bluce (Yunxia Lin), If there is something wrong or GNN-related issue, welcome to send email (Address: [email protected], [email protected]).
Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder,…
GNN and its variants are an emerging and powerful neural network approach. Its application is no longer limited to the original field. It has flourished in many other areas, such as Data Visualization, Image Processing, NLP, Recommendation System, Computer Vision, Bioinformatics, Chemical informatics, Drug Development and Discovery, Smart Transportation.
Very hot research topic: the representative work–Graph convolutional networks (GCNs) proposed by T.N. Kipf and M. Welling (ICLR2017 [5] in conference paper list) has been cited 1,020 times in Google Scholar (on 09 May 2019). Update: 1, 065 times (on 20 May 2019); Update: 1, 106 times (on 27 May 2019); Update: 1, 227 times (on 19 June 2019); Update: 1, 377 times (on 8 July 2019); Update: 1, 678 times (on 17 Sept. 2019); Update: 1, 944 times (on 29 Oct. 2019); Update: 2,232 times (on 9 Dec. 2019)
Thanks for giving us so many stars and supports from many developers and scientists on Github!!! We will continue to make this project better.
Project Start time: 11 Dec 2018, Latest updated time: 9 Dec. 2019
More papers about GNN models and their applications will come from AAAI2020 … We are waiting for the paper to be released.
The categorization of deep learning methods on graphs[1] from Tsinghua University.
Some typical application of GNN[2] from Tsinghua University.
Some open-source codes of the state-of-the-art methods[3].
F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. paper.
Scarselli F, Gori M, Tsoi A C, et al. Computational capabilities of graph neural networks, IEEE Transactions on Neural Networks, 2009. paper.
Micheli A . Neural Network for Graphs: A Contextual Constructive Approach. IEEE Transactions on Neural Networks, 2009. paper.
Goles, Eric, and Gonzalo A. Ruz. Dynamics of Neural Networks over Undirected Graphs. Neural Networks, 2015. paper.
Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, Deep Learning of Graphs with Ngram Convolutional Neural Networks, IEEE Transactions on Knowledge & Data Engineering, 2017. paper. code.
Petroski Such F , Sah S , Dominguez M A , et al. Robust Spatial Filtering with Graph Convolutional Neural Networks. IEEE Journal of Selected Topics in Signal Processing, 2017. paper.
Kawahara J, Brown C J, Miller S P, et al. BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 2017. paper.
Muscoloni A , Thomas J M , Ciucci S , et al. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nature Communications, 2017. paper.
D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, Next-Generation Machine Learning for Biological Networks, Cell, 2018. paper.
Marinka Z , Monica A , Jure L . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018. paper.
Sarah P , Ira K S , Enzo F , et al. Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease. Medical Image Analysis, 2018. paper.
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert, Metric learning with spectral graph convolutions on brain connectivity networks, NeuroImage, 2018. paper.
Xie T , Grossman J C . Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters, 2018. paper.
Phan, Anh Viet, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. DGCNN: A Convolutional Neural Network over Large-Scale Labeled Graphs. Neural Networks, 2018. paper
Song T, Zheng W, Song P, et al. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2018. paper
Levie R, Monti F, Bresson X, et al. Cayleynets: Graph convolutional neural networks with complex rational spectral filters. IEEE Transactions on Signal Processing 2019. paper
Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper
Qin A, Shang Z, Tian J, et al. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2019. paper
Coley C W, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 2019. paper
Zhang Z, Chen D, Wang Z, et al. Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition, 2019. paper
Hong Y, Kim J, Chen G, et al. Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE transactions on medical imaging, 2019. paper
Khodayar M, Mohammadi S, Khodayar M E, et al. Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting. IEEE Transactions on Sustainable Energy, 2019. paper
Zhang Q, Chang J, Meng G, et al. Learning graph structure via graph convolutional networks. Pattern Recognition, 2019. paper
Xuan P, Pan S, Zhang T, et al. Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations. Cells, 2019. paper
Sun M, Zhao S, Gilvary C, et al. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics, 2019. paper
Spier N, Nekolla S, Rupprecht C, et al. Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks. Scientific reports, 2019. paper
Heyuan Shi, et al. Hypergraph-Induced Convolutional Networks for Visual Classification. IEEE Transactions on Neural Networks and Learning Systems, 2019. paper
S.Pan, et al. Learning Graph Embedding With Adversarial Training Methods. IEEE Transactions on Cybernetics, 2019. paper
D. Grattarola, et al. Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds. IEEE Transactions on Neural Networks and Learning Systems. paper
Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints, NeurIPS(NIPS) 2015. paper. code.
M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016. paper.
S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, AAAI 2016. paper.
M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NeurIPS(NIPS) 2016. paper. code.
T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. paper. code.
A. Fout, B. Shariat, J. Byrd, A. Benhur, Protein Interface Prediction using Graph Convolutional Networks, NeurIPS(NIPS) 2017. paper.
Monti F, Bronstein M, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks, NeurIPS(NIPS) 2017. paper.
Simonovsky M, Komodakis N. Dynamic edgeconditioned filters in convolutional neural networks on graphs, CVPR. 2017. paper
R. Li, S. Wang, F. Zhu, J. Huang, Adaptive Graph Convolutional Neural Networks, AAAI 2018. paper
J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, NeurIPS(NIPS) 2018. paper.
C. Zhuang, Q. Ma, Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification, WWW 2018. paper
H. Gao, Z. Wang, S. Ji, Large-Scale Learnable Graph Convolutional Networks, KDD 2018. paper
D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial Attacks on Neural Networks for Graph Data, KDD 2018. paper
Ying R , He R , Chen K , et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, ICLR, 2018. paper
Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper
Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. IJCAI 2018. paper
Chen J , Zhu J , Song L . Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper
Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, RGCNN: Regularized Graph CNN for Point Cloud Segmentation. ACM Multimedia 2018. paper, code,
Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. Dating Documents using Graph Convolution Networks. ACL 2018. paper, code
Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. Graph networks as learnable physics engines for inference and control. ICML 2018. paper
Muhan Zhang, Yixin Chen. Link Prediction Based on Graph Neural Networks. NeurIPS(NIPS) 2018. paper
Chen, Jie, Tengfei Ma, and Cao Xiao. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper
Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. IJCAI 2018. paper
Rahimi A , Cohn T , Baldwin T . Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper
Morris C , Ritzert M , Fey M , et al.Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.. AAAI 2019. paper
Xu K, Hu W, Leskovec J, et al. How Powerful are Graph Neural Networks?, ICLR 2019. paper
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR 2019. paper
Daniel Zügner, Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR 2019. paper
Zhang Xinyi, Lihui Chen. Capsule Graph Neural Network, ICLR 2019. paper
Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. LanczosNet: Multi-Scale Deep Graph Convolutional Networks, ICLR 2019, paper
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. Graph Wavelet Neural Network, ICLR 2019, paper
Hu J, Guo C, Yang B, et al. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks ICDE. 2019. paper
Yao L, Mao C, Luo Y . Graph Convolutional Networks for Text Classification. AAAI 2019. paper
Landrieu L , Boussaha M . Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. CVPR 2019. paper
Si C , Chen W , Wang W , et al. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper
Cucurull G , Taslakian P , Vazquez D . Context-Aware Visual Compatibility Prediction. CVPR 2019. paper
Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper
Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper
Arushi Goel, Keng Teck Ma, Cheston Tan. An End-to-End Network for Generating Social Relationship Graphs. CVPR 2019. paper
Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search. CVPR 2019 paper
Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019 paper
Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. Learning to Cluster Faces on an Affinity Graph. CVPR 2019 paper
Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. KDD2019, paper
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW2019, paper
Kim J, Kim T, Kim S, et al. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper
Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS. ICLR 2019. paper
Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. ncRNA Classification with Graph Convolutional Networks. SIGKDD 2019. paper
Wu F, Zhang T, Souza Jr A H, et al. Simplifying Graph Convolutional Networks. ICML 2019. paper.
Junhyun Lee, Inyeop Lee, Jaewoo Kang. Self-Attention Graph Pooling. ICML 2019. paper.
Chiang W L, Liu X, Si S, et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. SIGKDD 2019. paper.
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. SIGKDD 2019. paper.
Wu S, Tang Y, Zhu Y, et al. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper.
Qu M, Bengio Y, Tang J. GMNN: Graph Markov Neural Networks. ICML 2019. papercoder.
Li Y, Gu C, Dullien T, et al. Graph Matching Networks for Learning the Similarity of Graph Structured Objects, ICML 2019.paper.
Gao H, Ji S. Graph U-Nets, ICML 2019. paper.
Bojchevski A, Günnemann S. Adversarial Attacks on Node Embeddings via Graph Poisoning, ICML 2019. paper.
Jeong D, Kwon T, Kim Y, et al. Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. ICML 2019. paper.
Zhang G, He H, Katabi D. Circuit-GNN: Graph Neural Networks for Distributed Circuit Design. ICML 2019. paper.
Alet F, Jeewajee A K, Bauza M, et al. Graph Element Networks: adaptive, structured computation and memory, ICML 2019. paper.
Rieck B, Bock C, Borgwardt K. A Persistent Weisfeiler-Lehman Procedure for Graph Classification, ICML 2019. paper.
Walker I, Glocker B. Graph Convolutional Gaussian Processes,ICML 2019. paper.
Yu Y, Chen J, Gao T, et al. DAG-GNN: DAG Structure Learning with Graph Neural Networks, ICML 2019. paper.
Zhijiang Guo, Yan Zhang and Wei Lu, Attention Guided Graph Convolutional Networks for Relation Extraction ACL 2019. paper. coder.
Chang Li, Dan Goldwasser. Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media ACL 2019. paper.
Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun. Graph Neural Networks with Generated Parameters for Relation Extraction ACL 2019. paper.
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar. Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks ACL 2019. paper.
Cui Z, Li Z, Wu S, et al. Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks WWW 2019. paper.
Zhang, Chris, et al. Graph HyperNetworks for Neural Architecture Search. ICLR 2019. paper.
Chen, Zhengdao, et al. Supervised Community Detection with Line Graph Neural Networks. ICLR 2019. paper.
Maron, Haggai, et al. Invariant and Equivariant Graph Networks. ICLR 2019. paper.
Gulcehre, Caglar, et al. Hyperbolic Attention Networks. ICLR, 2019. paper.
Prates, Marcelo O. R., et al. Learning to Solve NP-Complete Problems – A Graph Neural Network for the Decision TSP. AAAI, 2019. paper.
Liu, Ziqi, et al. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. AAAI, 2019. paper.
Keriven N, Peyré G. Universal invariant and equivariant graph neural networks. NeurIPS, 2019. paper.
Qi Liu, et al. Hyperbolic Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.
Zhitao Ying, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.
Yaqin Zhou, et al. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.
Ehsan Hajiramezanali, et al. Variational Graph Recurrent Neural Networks. NeurIPS, 2019. The paper is not yet available.
Sitao Luan, et al. Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS, 2019. The paper is not yet available.
Difan Zou, et al. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS, 2019. The paper is not yet available.
Seongjun Yun, et al. Graph Transformer Networks. NeurIPS, 2019. The paper is not yet available.
Andrei Nicolicioiu, et al. Recurrent Space-time Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.
Nima Dehmamy, et al. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. NeurIPS, 2019. The paper is not yet available.
Maxime Gasse, et al. Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS, 2019. The paper is not yet available.
Zhengdao Chen, et al. On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS, 2019. The paper is not yet available.
Vineet Kosaraju, et al. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. NeurIPS, 2019. The paper is not yet available.
Carl Yang, et al.Conditional Structure Generation through Graph Variational Generative Adversarial Nets. NeurIPS, 2019. The paper is not yet available.
Naganand Yadati, et al.HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. NeurIPS, 2019. The paper is not yet available.
Haggai Maron, et al.Provably Powerful Graph Networks. NeurIPS, 2019. The paper is not yet available.
Eliya Nachmani, et al.Hyper-Graph-Network Decoders for Block Codes. NeurIPS, 2019. The paper is not yet available.
Hanjun Dai, et al.Learning Transferable Graph Exploration. NeurIPS, 2019. The paper is not yet available.
Ryoma Sato, et al.Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS, 2019. The paper is not yet available.
Boris Knyazev, et al.Understanding attention in graph neural networks. NeurIPS, 2019. The paper is not yet available.
Renjie Liao, et al.Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS, 2019. The paper is not yet available.
Bryan Wilder, et al.End to end learning and optimization on graphs. NeurIPS, 2019. The paper is not yet available.
Simon Du, et al.Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS, 2019. The paper is not yet available.
W. O. K. Asiri Suranga Wijesinghe, et al. DFNets: Spectral CNNs for Graphs with Feedback-looped Filters. NeurIPS, 2019. The paper is not yet available.
Dong Wook Shu, et al.3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. ICCV 2019. paper
Yujun Cai, et al. Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks. ICCV 2019. paper
Runhao Zeng, et al. Graph Convolutional Networks for Temporal Action Localization. ICCV 2019. paper
Yin Bi, et al. Graph-Based Object Classification for Neuromorphic Vision Sensing. ICCV 2019. paper
103.Tianshui Chen, et al. Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition. ICCV 2019. paper
Linjie Li, et al. Relation-Aware Graph Attention Network for Visual Question Answering. ICCV 2019. paper
Jiwoong Park, et al. Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning. ICCV 2019. paper
Runzhong Wang, et al. Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV 2019. paper
Zhiqiang Tao, et al. Adversarial Graph Embedding for Ensemble Clustering. IJCAI 2019. paper
Xiaotong Zhang, et al. Attributed Graph Clustering via Adaptive Graph Convolution. IJCAI 2019. paper
Jianwen Jiang, et al. Dynamic Hypergraph Neural Networks. IJCAI 2019. paper
Hogun Park, et al. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. IJCAI 2019. paper
Hao Peng, et al. Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. IJCAI 2019. paper
Chengfeng Xu, et al. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper
Ruiqing Xu, et al. Graph Convolutional Network Hashing for Cross-Modal Retrieval. IJCAI 2019. paper
Bingbing Xu, et al. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning. IJCAI 2019. paper
Zonghan Wu, et al. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper
Fenyu Hu, et al. Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. IJCAI 2019. paper
Li Zheng, et al. AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN. IJCAI 2019. paper
Liang Yang, et al. Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology. IJCAI 2019. paper
Liang Yang, et al. Masked Graph Convolutional Network. IJCAI 2019. paper
Xiaofeng Xu, et al. Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation. IJCAI 2019. paper
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Park C, Lee C, Bahng H, et al. STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. AAAI 2020. paper.
Liu Y, Wang X, Wu S, et al. Independence Promoted Graph Disentangled Networks. AAAI 2020. paper.
Shi H, Fan H, Kwok J T. Effective Decoding in Graph Auto-Encoder using Triadic Closure. AAAI 2020. paper.
Wang X, Wang R, Shi C, et al. Multi-Component Graph Convolutional Collaborative Filtering. AAAI 2020. paper.
Su J, Beling P A, Guo R, et al. Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration. AAAI 2020. paper.
Claudio Gallicchio and Alessio Micheli. Fast and Deep Graph Neural Networks. AAAI 2020. paper.
Peng W, Hong X, Chen H, et al. Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching. AAAI 2020. paper.
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Wang Y , Sun Y , Liu Z , et al. Dynamic Graph CNN for Learning on Point Clouds. arXiv 2018. paper.
Ryu S , Lim J , Hong S H , et al. Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network. arXiv 2018. paper.
Cui Z , Henrickson K , Ke R , et al. High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arXiv 2018. paper.
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Bai Y , Ding H , Bian S , et al. Graph Edit Distance Computation via Graph Neural Networks. arXiv 2018. paper.
Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network. arXiv 2018. paper.
Matthew Baron, Topology and Prediction Focused Research on Graph Convolutional Neural Networks. arXiv 2018. paper.
Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, When Work Matters: Transforming Classical Network Structures to Graph CNN. arXiv 2018. paper.
Xavier Bresson, Thomas Laurent, Residual Gated Graph ConvNets. arXiv 2018. paper.
Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. arXiv 2018. paper.
Xiaojie GuoLingfei WuLiang Zhao. Deep Graph Translation. arXiv 2018. paper.
Choma, Nicholas, et al. Graph Neural Networks for IceCube Signal Classification. ArXiv 2018. paper.
Tyler Derr, Yao Ma, Jiliang Tang. Signed Graph Convolutional Network ArXiv 2018. paper.
Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning ArXiv 2018. paper.
Sun K, Koniusz P, Wang J. Fisher-Bures Adversary Graph Convolutional Networks. arXiv 2019. paper.
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Lemos H, Prates M, Avelar P, et al. Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems. arXiv 2019. paper.
Diehl F, Brunner T, Le M T, et al. Graph Neural Networks for Modelling Traffic Participant Interaction. arXiv 2019. paper.
Murphy R L, Srinivasan B, Rao V, et al. Relational Pooling for Graph Representations. arXiv 2019. paper.
Zhang W, Shu K, Liu H, et al. Graph Neural Networks for User Identity Linkage. arXiv 2019. paper.
Ruiz L, Gama F, Ribeiro A. Gated Graph Convolutional Recurrent Neural Networks. arXiv 2019. paper.
Phillips S, Daniilidis K. All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks. arXiv 2019. paper.
Hu F, Zhu Y, Wu S, et al. Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. arXiv 2019. paper.
Deng Z, Dong Y, Zhu J. Batch Virtual Adversarial Training for Graph Convolutional Networks. arXiv 2019. paper.
Chen Z M, Wei X S, Wang P, et al.Multi-Label Image Recognition with Graph Convolutional Networks. arXiv 2019. paper.
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Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. PiNet: A Permutation Invariant Graph Neural Network for Graph Classification. arXiv 2019. paper.
Padraig Corcoran. Function Space Pooling For Graph Convolutional Networks. arXiv 2019. paper.
Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. Joint embedding of structure and features via graph convolutional networks. arXiv 2019. paper.
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Gao X, Xiong H, Frossard P. iPool–Information-based Pooling in Hierarchical Graph Neural Networks. arXiv 2019. paper.
Zhou K, Song Q, Huang X, et al. Auto-GNN: Neural Architecture Search of Graph Neural Networks. arXiv 2019. paper.
DGL is developed and maintained by New York University, New York University Shanghai, AWS Shanghai Research Institute and AWS MXNet Science Team.
Initiation time: 2018.
Source: URL, github
NGra is developed and maintained by Peking University and Microsoft Asia Research Institute.
Initiation time:2018
Source: pdf
Graph_nets is developed and maintained by DeepMind, Google Corp.
Initiation time:2018
Source: github
Euler is developed and maintained by Alimama, which belongs to Alibaba Group.
Initiation time:2019
Source: github
PyTorch Geometric is developed and maintained by TU Dortmund University, Germany.
Initiation time:2019
Source: github paper
PBG is developed and maintained by Facebook AI Research.
Initiation time:2019
Source: github paper
Angel is developed and maintained by Tencent Inc.
Initiation time:2019
Source: github
Plato is developed and maintained by Tencent Inc.
Initiation time:2019
Source: github
PGL is developed and maintained by Baidu Inc.
Initiation time:2019
Source: github