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READMEhttps://github.com/SongFGH/graph
https://github.com/SongFGH/graph#该文是对以下三篇graph综述进行的整理-主要是对gnn网络的分类gnn在cv上的应用以及细节的地方添加了自己的理解可能会有些error若有修改意见可以提出
https://github.com/SongFGH/graph#总结性质的
https://github.com/SongFGH/graph#github上的某篇总结-介绍了相关的论文博客以及研究者
https://github.com/sungyongs/graph-based-nnhttps://github.com/sungyongs/graph-based-nn
https://github.com/thunlp/GNNPapershttps://github.com/thunlp/GNNPapers
Spatio-temporal modeling 论文列表(主要是graph convolution相关)https://github.com/Eilene/spatio-temporal-paper-list
https://mp.weixin.qq.com/s/xgf7A3GFh1cIM2QhaCyyoAhttps://mp.weixin.qq.com/s/xgf7A3GFh1cIM2QhaCyyoA
https://github.com/SongFGH/graph#综述论文-
Deep Learning on Graphs: A Surveyhttps://arxiv.org/abs/1812.04202
[新智元解读]https://mp.weixin.qq.com/s/eelcT5x_kWC0dDt0_Ph4qg
Graph Neural Networks: A Review of Methods and Applicationshttps://arxiv.org/abs/1812.08434
[新智元解读]https://mp.weixin.qq.com/s/h4jQWJlQV2Ew3SpuF8k5Hw
A Comprehensive Survey on Graph Neural Networkshttps://arxiv.org/abs/1901.00596
Relational inductive biases, deep learning, and graph networkshttps://arxiv.org/pdf/1806.01261.pdf
Geometric Deep Learning: Going beyond Euclidean datahttps://arxiv.org/pdf/1611.08097.pdf
Computational Capabilities of Graph Neural Networkshttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4703190
Neural Message Passing for Quantum Chemistryhttps://arxiv.org/pdf/1704.01212.pdf
Non-local Neural Networkshttp://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Non-Local_Neural_Networks_CVPR_2018_paper.pdf
The Graph Neural Network Modelhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4700287
https://github.com/SongFGH/graph#library
[github]https://github.com/rusty1s/pytorch_geometric
[website]https://www.dgl.ai/
[github]https://github.com/dmlc/dgl
[github]https://github.com/deepmind/graph_nets
https://github.com/SongFGH/graph#谱上的图卷积发展spectral-based-graph-convolutional-networks
The Emerging Field of Signal Processing on Graphshttps://arxiv.org/pdf/1211.0053.pdf
Spectral Networks and Locally Connected Networks on Graphshttps://arxiv.org/abs/1312.6203
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringhttps://arxiv.org/abs/1606.09375
[PyTorch Code]https://github.com/xbresson/graph_convnets_pytorch/blob/master/README.md
[TF Code]https://github.com/mdeff/cnn_graph
Semi-Supervised Classification with Graph Convolutional Networkshttps://arxiv.org/abs/1609.02907
[Code]https://github.com/tkipf/gcn
[Blog]http://tkipf.github.io/graph-convolutional-networks/
Deep convolutional networks on graph-structured datahttps://arxiv.org/abs/1506.05163
Adaptive graph convolutional neural networkshttps://arxiv.org/abs/1801.03226
Cayleynets: Graph convolutional neural networks with complex rational spectral filtershttps://arxiv.org/abs/1705.07664
https://github.com/SongFGH/graph#空间上的图卷积spatial-based-graph-convolutional-networks
Graph Attention Network (GAT)https://arxiv.org/abs/1710.10903
[tf code]https://github.com/PetarV-/GAT
Inductive representation learning on large graphs(GraphSAGE)http://papers.NeurIPS.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
[tf code]https://github.com/williamleif/GraphSAGE
Neural Message Passing for Quantum Chemistryhttps://arxiv.org/pdf/1704.01212.pdf
Learning convolutional neural networks for graphshttps://arxiv.org/abs/1605.05273
Geometric deep learning on graphs and manifolds using mixture model cnnshttp://openaccess.thecvf.com/content_cvpr_2017/papers/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf
Learning convolutional neural networks for graphshttp://proceedings.mlr.press/v48/niepert16.pdf
Large-scale learnable graph convolutional networks (LGCN)https://dl.acm.org/citation.cfm?id=3219947
[tf code]https://github.com/divelab/lgcn/
Diffusion-convolutional neural networkshttps://arxiv.org/abs/1511.02136
[tf code]https://github.com/liyaguang/DCRNN
Geometric deep learning on graphs and manifolds using mixture model cnnshttps://arxiv.org/abs/1611.08402
https://github.com/SongFGH/graph#谱上与空间gcn的比较comparison-between-spectral-and-spatial-models
改善GCN在训练方面的缺陷: Training Methodshttps://github.com/SongFGH/graph#%E6%94%B9%E5%96%84gcn%E5%9C%A8%E8%AE%AD%E7%BB%83%E6%96%B9%E9%9D%A2%E7%9A%84%E7%BC%BA%E9%99%B7-training-methods
输入含有边特征的GNN：input allow edge featureshttps://github.com/SongFGH/graph#%E8%BE%93%E5%85%A5%E5%90%AB%E6%9C%89%E8%BE%B9%E7%89%B9%E5%BE%81%E7%9A%84gnninput-allow-edge-features
https://github.com/SongFGH/graph#改善gcn在训练方面的缺陷-training-methods-
1stChebNet(semi-supervised GCN)https://arxiv.org/abs/1609.02907
Fastgcn: fast learning with graph convolutional networks via importance sampling (ICLR 2018)https://arxiv.org/abs/1801.10247
Stochastic training of graph convolutional networks with variance reduction (ICML 2018)https://arxiv.org/abs/1710.10568
Adaptive sampling towards fast graph representation learning (NeurIPS 2018)https://arxiv.org/abs/1809.05343
Inductive representation learning on large graphs (NeurIPS 2017)https://arxiv.org/abs/1706.02216
Fastgcn: fast learning with graph convolutional networks via importance sampling (ICLR 2018)https://arxiv.org/abs/1801.10247
Adaptive sampling towards fast graph representation learning (NeurIPS 2018)https://arxiv.org/abs/1809.05343
Stochastic training of graph convolutional networks with variance reduction (ICML 2018)https://arxiv.org/abs/1710.10568
Deeper insights into graph convolutional networks for semi-supervised learning (arXiv:1801.07606, 2018)https://arxiv.org/abs/1801.07606
Inductive representation learning on large graphs (NeurIPS 2017)https://arxiv.org/abs/1706.02216
Fastgcn: fast learning with graph convolutional networks via importance sampling (ICLR 2018)https://arxiv.org/abs/1801.10247
Stochastic training of graph convolutional networks with variance reduction (ICML 2018)https://arxiv.org/abs/1710.10568
https://github.com/SongFGH/graph#graph-attention-networks
Graph Attention Network (GAT)https://arxiv.org/abs/1710.10903
[tf code]https://github.com/PetarV-/GAT
Gaan:Gated attention networks for learning on large and spatiotemporal graphshttps://arxiv.org/abs/1803.07294
Graph classification using structural attentionhttp://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf
Watch your step: Learning node embeddings via graph attentionhttps://arxiv.org/abs/1710.09599
https://github.com/SongFGH/graph#gated-graph-neural-network
https://github.com/SongFGH/graph#residual-and-jumping-connectionsskip-connections
Go deeper?https://github.com/SongFGH/graph/blob/master
https://github.com/SongFGH/graph#graph-auto-encoders
https://github.com/ShiYaya/graph/blob/master/images/graph-auto-encoder.png
Variational graph auto-encoders (GAE)https://arxiv.org/abs/1611.07308
[tkipf/code]https://github.com/tkipf/gae
[tf code]https://github.com/limaosen0/Variational-Graph-Auto-Encoders
Adversarially regularized graph autoencoder for graph embedding (ARGA)https://arxiv.org/abs/1611.07308
[tf code]https://github.com/Ruiqi-Hu/ARGA
Learning deep network representations with adversarially regularized autoencoders (NetRA)http://www.cs.ucsb.edu/~bzong/doc/kdd-18.pdf
Deep neural networks for learning graph representations (DNGR)https://pdfs.semanticscholar.org/1a37/f07606d60df365d74752857e8ce909f700b3.pdf
[matlab code]https://github.com/ShelsonCao/DNGR
Structural deep network embedding (SDNE)https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf
[python code]https://github.com/suanrong/SDNE
Deep recursive network embedding with regular equivalence (DRNE)http://pengcui.thumedialab.com/papers/NE-RegularEquivalence.pdf
https://github.com/tadpole/DRNEhttps://github.com/tadpole/DRNE
https://github.com/SongFGH/graph#graph-generative-networks-
Graphrnn: A deep generative model for graphshttps://arxiv.org/abs/1802.08773
[tf code]https://github.com/snap-stanford/GraphRNN
Learning deep generative models of graphshttps://arxiv.org/abs/1803.03324
Molgan: An implicit generative model for small molecular graphshttps://arxiv.org/pdf/1805.11973.pdf
Net-gan: Generating graphs via random walkshttps://arxiv.org/abs/1803.00816
https://github.com/SongFGH/graph#gcn-based-graph-spatial-temporal-networks-
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (DCRNN)https://arxiv.org/abs/1707.01926
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting (CNN-GNN)https://arxiv.org/abs/1709.04875
tf codehttps://github.com/VeritasYin/STGCN_IJCAI-18
Spatial temporal graph convolutional networks for skeleton-based action recognition (ST-GCN)https://arxiv.org/abs/1801.07455
[pytorch code]https://github.com/yysijie/st-gcn
Structural-rnn:Deep learning on spatio-temporal graphs (Structural-RNN)https://arxiv.org/abs/1511.05298
[theano code]https://github.com/asheshjain399/RNNexp
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learninghttps://arxiv.org/abs/1805.02335
Spatial temporal graph convolutional networks for skeleton-based action recognition (ST-GCN)https://arxiv.org/abs/1801.07455
[pytorch code]https://github.com/yysijie/st-gcn
https://github.com/SongFGH/graph#graph-recurrent-neural-networks
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networkshttps://arxiv.org/abs/1804.00823
Structured Sequence Modeling with Graph Convolutional Recurrent Networkshttps://arxiv.org/abs/1804.00823
https://github.com/SongFGH/graph#graph-reinforcement-learning
https://github.com/SongFGH/graph#输入含有边特征的gnninput-allow-edge-features---
The graph neural network model(GNN)http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1015.7227&rep=rep1&type=pdf
Neural message passing for quantum chemistry(MPNN)https://arxiv.org/abs/1704.01212
Diffusion-convolutional neural networks(DCNN)https://arxiv.org/abs/1511.02136
Learning convolutional neural networks for graphs(PATCHY-SAN)https://arxiv.org/abs/1605.05273
Geniepath:Graph neural networks with adaptive receptive pathshttps://arxiv.org/pdf/1802.00910.pdf
Dual graph convolutional networks for graph-based semi-supervised classificationhttps://dl.acm.org/citation.cfm?id=3186116
Signed graph convolutional networkhttps://arxiv.org/abs/1808.06354
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelinghttps://arxiv.org/abs/1703.04826
Exploring Visual Relationship for Image Captioninghttps://arxiv.org/abs/1809.07041
https://github.com/SongFGH/graph#图表达graph-level-representationreadout-operations--
Convolutional networks on graphs for learning molecular fingerprintshttps://arxiv.org/abs/1509.09292
Diffusion-convolutional neural networkshttps://arxiv.org/abs/1511.02136
Molecular graph convolutions: moving beyond fingerprintshttps://arxiv.org/abs/1603.00856
Spectral networks and locally connected networks on graphshttps://arxiv.org/abs/1312.6203
Spectral networks and locally connected networks on graphshttps://arxiv.org/abs/1312.6203
Deep convolutional networks on graph-structured datahttps://arxiv.org/abs/1506.05163
Hierarchical Graph Representation Learning with Differentiable Poolinghttps://arxiv.org/pdf/1806.08804.pdf
[code]https://github.com/RexYing/diffpool
Convolutional neural networks on graphs with fast localized spectral filteringhttps://arxiv.org/abs/1606.09375
Deep convolutional networks on graph-structured datahttps://arxiv.org/abs/1506.05163
An end-to-end deep learning architecture for graph classificationhttps://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf
[code]https://github.com/muhanzhang/DGCNN
[pytorch code]https://github.com/muhanzhang/pytorch_DGCNN
Hierarchical graph representation learning with differentiable poolinghttps://arxiv.org/abs/1806.08804
[code]https://github.com/RexYing/diffpool
https://github.com/SongFGH/graph#gcn的应用
https://github.com/SongFGH/graph#计算机视觉--
https://github.com/ShiYaya/graph/blob/master/images/gcn-in-image-application.png
https://github.com/SongFGH/graph#scene-graph-generation
https://github.com/SongFGH/graph#point-clouds-classification-and-segmentation
https://github.com/SongFGH/graph#action-recognition--
Spatial temporal graph convolutional networks for skeleton-based action recognition (ST-GCN)https://arxiv.org/abs/1801.07455
[pytorch code]https://github.com/yysijie/st-gcn
Structural-rnn:Deep learning on spatio-temporal graphs (Structural-RNN)https://arxiv.org/abs/1511.05298
[theano code]https://github.com/asheshjain399/RNNexp
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learninghttps://arxiv.org/abs/1805.02335
https://github.com/SongFGH/graph#image-classification--
Few-shot learning with graph neural networkshttps://arxiv.org/abs/1711.04043
[code]https://github.com/vgsatorras/few-shot-gnn
Zero-shot recognition via semantic embeddings and knowledge graphshttps://arxiv.org/abs/1803.08035
Multi-label zero-shot learning with structured knowledge graphshttps://arxiv.org/abs/1711.06526
Rethinking knowledge graph propagation for zero-shot learninghttps://arxiv.org/abs/1805.11724
The more you know: Using knowledge graphs for image classificationhttps://arxiv.org/abs/1612.04844
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learninghttps://arxiv.org/abs/1805.10002
[tf code]https://github.com/csyanbin/TPN
https://github.com/SongFGH/graph#few-shot
Few-shot learning with graph neural networkshttps://arxiv.org/abs/1711.04043
[code]https://github.com/vgsatorras/few-shot-gnn
Neural graph matching networks for fewshot 3d action recognitionhttp://openaccess.thecvf.com/content_ECCV_2018/html/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.html
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learninghttps://arxiv.org/abs/1805.10002
[tf code]https://github.com/csyanbin/TPN
https://github.com/SongFGH/graph#zero-shot
Zero-shot recognition via semantic embeddings and knowledge graphshttps://arxiv.org/abs/1803.08035
Multi-label zero-shot learning with structured knowledge graphshttps://arxiv.org/abs/1711.06526
Rethinking knowledge graph propagation for zero-shot learninghttps://arxiv.org/abs/1805.11724
https://github.com/SongFGH/graph#semantic-segmentation
https://github.com/SongFGH/graph#visual-question-answer--
paperhttps://arxiv.org/pdf/1706.01427.pdf
paperhttps://arxiv.org/pdf/1609.05600.pdf
paperhttp://papers.NeurIPS.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf
paperhttps://arxiv.org/pdf/1806.07243
[code]https://github.com/aimbrain/vqa-project
https://github.com/SongFGH/graph#object-detection
https://github.com/SongFGH/graph#interaction-detection
https://github.com/SongFGH/graph#region-classification
https://github.com/SongFGH/graph#social-relationship-understanding
https://github.com/SongFGH/graph#自然语言处理
https://github.com/ShiYaya/graph/blob/master/images/gcn-in-test-application.png
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelinghttps://arxiv.org/abs/1703.04826
[官方code(theano 0.8.2,lasagne 0.1)]https://github.com/diegma/neural-dep-srl
[复现pytorch]https://github.com/kervyRivas/Graph-convolutional
专知讲解https://mp.weixin.qq.com/s/c6ZhSk4r3pvnjHsvpwkkSw
https://github.com/ShiYaya/graph/blob/master/images/gcn%2Bformulation.png
https://github.com/ShiYaya/graph/blob/master/images/syntactic-dependecy.png
https://github.com/SongFGH/graph#other-application
https://github.com/ShiYaya/graph/blob/master/images/gcn-in-other-application.png
https://github.com/SongFGH/graph#by-yaya-papers-ive-read-about-the-application-of-graph-on-cv-and-nlp--
https://github.com/SongFGH/graph#open-problems-and-future-direction--
[Deeper insights into graph convolutional networks for semi-supervised learning]https://arxiv.org/abs/1801.07606
Graph Networkshttps://arxiv.org/abs/1806.01261
Fastgcn: fast learning with graph convolutional networks via importance sampling (ICLR 2018)https://arxiv.org/abs/1801.10247
Stochastic training of graph convolutional networks with variance reduction (ICML 2018)https://arxiv.org/abs/1710.10568
Inductive representation learning on large graphs (NeurIPS 2017)https://arxiv.org/abs/1706.02216
Large-scale learnable graph convolutional networkshttps://arxiv.org/abs/1808.03965
https://github.com/SongFGH/graph#未提到的文章--
Pitfalls of Graph Neural Network Evaluationhttps://arxiv.org/abs/1811.05868
How Powerful are Graph Neural Networks?https://arxiv.org/abs/1810.00826
专知解读https://mp.weixin.qq.com/s/OnRB44tliuTFcjlmuRG3Xw
Readme https://github.com/SongFGH/graph#readme-ov-file
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Contributorshttps://github.com/SongFGH/graph/graphs/contributors
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