A Comprehensive Survey on Graph Neural Networks
Tóm tắt
Từ khóa
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
li, 2018, Combinatorial optimization with graph convolutional networks and guided tree search, Proc NeurIPS, 536
kriege, 2019, A survey on graph kernels, arXiv 1903 11835
navarin, 2017, Approximated neighbours MinHash graph node kernel, Proc ESANN, 1
pan, 2016, Tri-party deep network representation, Proc IJCAI, 1895
shervashidze, 2011, Weisfeiler–Lehman graph kernels, J Mach Learn Res, 12, 2539
vishwanathan, 2010, Graph kernels, J Mach Learn Res, 11, 1201
gilmer, 2017, Neural message passing for quantum chemistry, Proc ICML, 1263
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
vinyals, 2016, Order matters: Sequence to sequence for sets, Proc ICLR, 1
niepert, 2016, Learning convolutional neural networks for graphs, Proc ICML, 2014
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
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
cao, 2016, Deep neural networks for learning graph representations, Proc AAAI, 1145
xu, 2019, How powerful are graph neural networks, Proc ICLR, 1
veli?kovi?, 2019, Deep graph infomax, Proc ICLR, 1
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
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
van den berg, 2017, Graph convolutional matrix completion, arXiv 1706 02263
wu, 2016, Google’s neural machine translation system: Bridging the gap between human and machine translation, arXiv 1609 08144
lecun, 1995, Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 3361
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
chen, 2018, FastGCN: Fast learning with graph convolutional networks via importance sampling, Proc ICLR, 1
johnson, 2016, Learning graphical state transitions, Proc ICLR, 1
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
velickovic, 2017, Graph attention networks, Proc ICLR, 1
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
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
bojchevski, 2018, NetGAN: Generating graphs via random walks, Proc ICML, 1
carlson, 2010, Toward an architecture for never-ending language learning, Proc AAAI, 1306, 10.1609/aaai.v24i1.7519
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
shchur, 2018, Pitfalls of graph neural network evaluation, Proc NeurIPS Workshop, 1
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
li, 2018, Factorizable net: An efficient subgraph-based framework for scene graph generation, Proc ECCV, 346
kipf, 2016, Variational graph auto-encoders, Proc NIPS Workshop Bayesian Deep Learn, 1
li, 2018, Learning deep generative models of graphs, Proc ICML, 1
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
ma, 2018, Constrained generation of semantically valid graphs via regularizing variational autoencoders, Proc NeurIPS, 7110
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
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
douglas, 2011, The Weisfeiler–Lehman method and graph isomorphism testing, arXiv 1101 5211
weisfeiler, 1968, A reduction of a graph to a canonical form and an algebra arising during this reduction, Nauchno- Technicheskaya Informatsia, 2, 12
boscaini, 2016, Learning shape correspondence with anisotropic convolutional neural networks, Proc NIPS, 3189
maron, 2019, Invariant and equivariant graph networks, ICLRE, 1
lee, 2019, Self-attention graph pooling, Proc ICML, 3734
kusner, 2017, Grammar variational autoencoder, Proc ICML, 1945
goodfellow, 2014, Generative adversarial nets, Proc NIPS, 2672
wang, 2019, Heterogeneous graph attention network, Proc World Wide Web Conf (WWW), 2022, 10.1145/3308558.3313562
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
li, 2015, Gated graph sequence neural networks, Proc ICLR, 1
dai, 2018, Learning steady-states of iterative algorithms over graphs, Proc ICML, 1114
bruna, 2014, Spectral networks and locally connected networks on graphs, Proc ICLR, 1
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
gulrajani, 2017, Improved training of Wasserstein GANs, Proc NIPS, 5767
abu-el-haija, 2018, Watch your step: Learning node embeddings via graph attention, Proc NeurIPS, 9197
duvenaud, 2015, Convolutional networks on graphs for learning molecular fingerprints, Proc NIPS, 2224