A Comprehensive Survey on Graph Neural Networks

IEEE Transactions on Neural Networks and Learning Systems - Tập 32 Số 1 - Trang 4-24 - 2021
Zonghan Wu1, Shirui Pan2, Fengwen Chen1, Guodong Long1, Chengqi Zhang1, Philip S. Yu3
1Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia
2Faculty of Information Technology, Monash University, Clayton, VIC, Australia
3University of Illinois at Chicago, Chicago, IL USA

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Tài liệu tham khảo

choi, 2018, Mime: Multilevel medical embedding of electronic health records for predictive healthcare, Proc NeurIPS, 4548

nguyen, 2018, Graph convolutional networks with argument-aware pooling for event detection, Proc AAAI, 5900

10.1016/j.neuroimage.2016.09.046

10.1109/CVPR.2016.90

li, 2018, Combinatorial optimization with graph convolutional networks and guided tree search, Proc NeurIPS, 536

10.24963/ijcai.2019/872

10.1145/3097983.3098126

kriege, 2019, A survey on graph kernels, arXiv 1903 11835

navarin, 2017, Approximated neighbours MinHash graph node kernel, Proc ESANN, 1

10.24963/ijcai.2018/493

pan, 2016, Tri-party deep network representation, Proc IJCAI, 1895

10.1016/j.knosys.2018.03.022

10.1109/TKDE.2018.2807452

shervashidze, 2011, Weisfeiler–Lehman graph kernels, J Mach Learn Res, 12, 2539

vishwanathan, 2010, Graph kernels, J Mach Learn Res, 11, 1201

10.1145/2623330.2623732

10.1109/ICDM.2018.8626170

10.1109/TKDE.2018.2849727

gilmer, 2017, Neural message passing for quantum chemistry, Proc ICML, 1263

10.1109/TBDATA.2018.2850013

henaff, 2015, Deep convolutional networks on graph-structured data, arXiv 1506 05163

kipf, 2017, Semi-supervised classification with graph convolutional networks, Proc ICLR, 1

defferrard, 2016, Convolutional neural networks on graphs with fast localized spectral filtering, Proc NIPS, 3844

10.1109/TNN.2008.2010350

10.1109/TSP.2018.2879624

vinyals, 2016, Order matters: Sequence to sequence for sets, Proc ICLR, 1

niepert, 2016, Learning convolutional neural networks for graphs, Proc ICML, 2014

10.1109/TPAMI.2007.1115

atwood, 2016, Diffusion-convolutional neural networks, Proc NIPS, 1993

chen, 2018, Stochastic training of graph convolutional networks with variance reduction, Proc ICML, 941

huang, 2018, Adaptive sampling towards fast graph representation learning, Proc NeurIPS, 4563

10.18653/v1/N18-2078

10.18653/v1/D17-1209

10.18653/v1/P18-1026

10.18653/v1/P18-1150

10.1109/CVPR.2018.00756

10.18653/v1/D17-1159

narasimhan, 2018, Out of the box: Reasoning with graph convolution nets for factual visual question answering, Proc NeurIPS, 2655

guo, 2018, Neural graph matching networks for fewshot 3D action recognition, Proc ECCV, 673

10.24963/ijcai.2019/418

10.1109/ICCV.2017.556

10.1109/CVPR.2017.697

cao, 2016, Deep neural networks for learning graph representations, Proc AAAI, 1145

10.1145/3292500.3330925

xu, 2019, How powerful are graph neural networks, Proc ICLR, 1

veli?kovi?, 2019, Deep graph infomax, Proc ICLR, 1

10.1609/aaai.v33i01.33014424

ying, 2018, Hierarchical graph representation learning with differentiable pooling, Proc NeurIPS, 4801

li, 2018, Deeper insights into graph convolutional networks for semi-supervised learning, Proc AAAI, 1

zhang, 2018, An end-to-end deep learning architecture for graph classification, Proc AAAI, 1

li, 2018, Adaptive graph convolutional neural networks, Proc AAAI, 3546

10.1145/3219819.3220077

allamanis, 2017, Learning to represent programs with graphs, Proc ICLR, 1

you, 2018, Graph convolutional policy network for goal-directed molecular graph generation, Proc NeurIPS, 6410

fout, 2017, Protein interface prediction using graph convolutional networks, Proc NIPS, 6530

monti, 2017, Geometric matrix completion with recurrent multi-graph neural networks, Proc NIPS, 3697

10.1145/3219819.3219890

van den berg, 2017, Graph convolutional matrix completion, arXiv 1706 02263

10.1145/2736277.2741093

wu, 2016, Google’s neural machine translation system: Bridging the gap between human and machine translation, arXiv 1609 08144

10.18653/v1/D15-1166

lecun, 1995, Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 3361

10.1109/MSP.2012.2205597

vincent, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371

yao, 2018, Deep multi-view spatial-temporal network for taxi demand prediction, Proc AAAI, 2588

10.1162/neco.1997.9.8.1735

chen, 2018, FastGCN: Fast learning with graph convolutional networks via importance sampling, Proc ICLR, 1

johnson, 2016, Learning graphical state transitions, Proc ICLR, 1

10.1109/MSP.2017.2693418

10.18653/v1/P18-1071

10.1109/SSCI.2018.8628758

10.1145/3219819.3219947

zhang, 2018, GaAN: Gated attention networks for learning on large and spatiotemporal graphs, Proc UAI, 1

bacciu, 2018, Contextual graph Markov model: A deep and generative approach to graph processing, Proc ICML, 1

hamilton, 2017, Inductive representation learning on large graphs, Proc NIPS, 1024

10.1145/3178876.3186116

10.1109/CVPR.2017.576

velickovic, 2017, Graph attention networks, Proc ICLR, 1

10.1145/1557019.1557109

chen, 2019, Alchemy: A quantum chemistry dataset for benchmarking AI models, arXiv 1906 09427

ramakrishnan, 2014, Quantum chemistry structures and properties of 134 kilo molecules, Data Science Journal, 1

10.1093/bioinformatics/btg130

10.1109/CVPR.2016.573

li, 2018, Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, Proc ICLR, 1

seo, 2018, Structured sequence modeling with graph convolutional recurrent networks, Proc NeurIPS, 362

10.1145/2611567

bojchevski, 2018, NetGAN: Generating graphs via random walks, Proc ICML, 1

10.1109/5.726791

10.24963/ijcai.2019/264

10.1609/aaai.v33i01.3301922

carlson, 2010, Toward an architecture for never-ending language learning, Proc AAAI, 1306, 10.1609/aaai.v24i1.7519

10.24963/ijcai.2018/505

yan, 2018, Spatial temporal graph convolutional networks for skeleton-based action recognition, Proc AAAI, 1

wang, 2019, Deep graph library: Towards efficient and scalable deep learning on graphs, Proc ICLR Workshop Represent Learn Graphs Manifolds, 1

10.1145/3132847.3132967

10.1109/TKDE.2015.2492567

shchur, 2018, Pitfalls of graph neural network evaluation, Proc NeurIPS Workshop, 1

10.1109/TCYB.2016.2526058

errica, 2020, A fair comparison of graph neural networks for graph classification, Proc ICLR, 1

kawamoto, 2018, Mean-field theory of graph neural networks in graph partitioning, Proc NeurIPS, 4362

zhang, 2018, Link prediction based on graph neural networks, Proc NeurIPS, 5165

yang, 2018, Graph R-CNN for scene graph generation, Proc ECCV, 690

10.1109/CVPR.2017.330

10.1145/2939672.2939753

li, 2018, Factorizable net: An efficient subgraph-based framework for scene graph generation, Proc ECCV, 346

10.24963/ijcai.2018/362

kipf, 2016, Variational graph auto-encoders, Proc NIPS Workshop Bayesian Deep Learn, 1

10.1145/3219819.3220068

10.1145/3219819.3220000

li, 2018, Learning deep generative models of graphs, Proc ICML, 1

10.1109/CVPR.2018.00133

you, 2018, GraphRNN: A deep generative model for graphs, Proc ICML, 1

wang, 2019, Dynamic graph CNN for learning on point clouds, ACM Trans Graph, 38, 1

simonovsky, 2018, Graphvae: Towards generation of small graphs using variational autoencoders, Proc ICANN, 412

10.1109/CVPR.2018.00479

ma, 2018, Constrained generation of semantically valid graphs via regularizing variational autoencoders, Proc NeurIPS, 7110

10.1145/3240508.3240621

ren, 2015, Faster R-CNN: Towards real-time object detection with region proposal networks, Proc NIPS, 91

de cao, 2018, MolGAN: An implicit generative model for small molecular graphs, ICML Workshop Theor Found Appl Deep Generative Models, 1

qi, 2018, Learning human-object interactions by graph parsing neural networks, Proc ECCV, 401

10.1109/CVPR.2016.91

satorras, 2018, Few-shot learning with graph neural networks, Proc ICLR, 1

pan, 2019, Learning graph embedding with adversarial training methods, IEEE Trans Cybern

pham, 2017, Column networks for collective classification, Proc AAAI, 2485

10.1145/1390156.1390294

douglas, 2011, The Weisfeiler–Lehman method and graph isomorphism testing, arXiv 1101 5211

10.1109/TNN.2008.2005141

weisfeiler, 1968, A reduction of a graph to a canonical form and an algebra arising during this reduction, Nauchno- Technicheskaya Informatsia, 2, 12

10.1162/0899766053491878

10.1109/CVPR.2018.00097

boscaini, 2016, Learning shape correspondence with anisotropic convolutional neural networks, Proc NIPS, 3189

10.1016/0893-6080(89)90020-8

10.1109/ICCVW.2015.112

maron, 2019, Invariant and equivariant graph networks, ICLRE, 1

10.1016/j.neunet.2018.08.010

lee, 2019, Self-attention graph pooling, Proc ICML, 3734

10.1021/acscentsci.7b00572

kusner, 2017, Grammar variational autoencoder, Proc ICML, 1945

goodfellow, 2014, Generative adversarial nets, Proc NIPS, 2672

10.1109/JSTSP.2017.2726981

wang, 2019, Heterogeneous graph attention network, Proc World Wide Web Conf (WWW), 2022, 10.1145/3308558.3313562

10.1109/CVPR.2017.11

10.1109/ICDM.2018.00113

hamilton, 2017, Representation learning on graphs: Methods and applications, Proc NIPS, 1024

battaglia, 2018, Relational inductive biases, deep learning, and graph networks, arXiv 1806 01261

boaz lee, 2018, Attention models in graphs: A survey, arXiv 1807 07984

10.1109/72.572108

10.1109/IJCNN.2005.1555942

10.1109/TNN.2008.2005605

10.1145/1401890.1402008

10.1109/IJCNN.2010.5596796

10.1109/MSP.2012.2235192

10.1609/aimag.v29i3.2157

li, 2015, Gated graph sequence neural networks, Proc ICLR, 1

10.3115/v1/D14-1179

dai, 2018, Learning steady-states of iterative algorithms over graphs, Proc ICML, 1114

10.1109/TSP.2015.2469645

10.1093/bioinformatics/btx252

bruna, 2014, Spectral networks and locally connected networks on graphs, Proc ICLR, 1

10.1109/TSP.2013.2238935

schlichtkrull, 2018, Modeling relational data with graph convolutional networks, ESWC, 593

dai, 2018, Syntax-directed variational autoencoder for structured data, Proc ICLR, 1

arjovsky, 2017, Wasserstein GAN, arXiv 1701 07875

10.1109/TNN.2004.837783

gulrajani, 2017, Improved training of Wasserstein GANs, Proc NIPS, 5767

10.1007/s10115-007-0103-5

abu-el-haija, 2018, Watch your step: Learning node embeddings via graph attention, Proc NeurIPS, 9197

10.1021/jm00106a046

10.1016/S0022-2836(03)00628-4

10.1093/bioinformatics/bti1007

duvenaud, 2015, Convolutional networks on graphs for learning molecular fingerprints, Proc NIPS, 2224

10.1007/s10822-016-9938-8

10.1038/ncomms13890

10.1145/3219819.3219980