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Title: GitHub - DeepGraphLearning/LiteratureDL4Graph: A comprehensive collection of recent papers on graph deep learning

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1   Node Representation Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#node-representation-learning
1.1   Unsupervised Node Representation Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#unsupervised-node-representation-learning
1.2   Node Representation Learning in Heterogeneous Graphshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#node-representation-learning-in-heterogeneous-graphs
1.3   Node Representation Learning in Dynamic Graphshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#node-representation-learning-in-dynamic-graphs
2   Knowledge Graph Embeddinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#knowledge-graph-embedding
3   Graph Neural Networkshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-neural-networks
4   Applications of Graph Deep Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#applications-of-graph-deep-learning
4.1   Natural Language Processinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#natural-language-processing
4.2   Computer Visionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#computer-vision
4.3   Recommender Systemshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#recommender-systems
4.4   Link Predictionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#link-prediction
4.5   Influence Predictionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#influence-prediction
4.6   Neural Architecture Searchhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#neural-architecture-search
4.7   Reinforcement Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#reinforcement-learning
4.8   Combinatorial Optimizationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#combinatorial-optimization
4.9   Adversarial Attack and Robustnesshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#adversarial-attack-and-robustness
4.10   Graph Matchinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-matching
4.11   Meta Learning and Few-shot Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#meta-learning-and-few-shot-learning
4.12   Structure Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#structure-learning
4.13   Bioinformatics and Chemistryhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#bioinformatics-and-chemistry
4.14   Graph Algorithmshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-algorithms
4.15   Theorem Provinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#theorem-proving
5   Graph Generationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-generation
6   Graph Layout and High-dimensional Data Visualizationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-layout-and-high-dimensional-data-visualization
7   Graph Representation Learning Systemshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#graph-representation-learning-systems
8   Datasetshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#datasets
1   Node Representation Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id3
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#1node-representation-learning
1.1   Unsupervised Node Representation Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id4
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#11unsupervised-node-representation-learning
DeepWalk: Online Learning of Social Representationshttps://arxiv.org/pdf/1403.6652
LINE: Large-scale Information Network Embeddinghttps://arxiv.org/pdf/1503.03578
GraRep: Learning Graph Representations with Global Structural Informationhttps://dl.acm.org/citation.cfm?id=2806512
node2vec: Scalable Feature Learning for Networkshttps://arxiv.org/pdf/1607.00653
Variational Graph Auto-Encodershttps://arxiv.org/abs/1611.07308
Scalable Graph Embedding for Asymmetric Proximityhttps://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696
Fast Network Embedding Enhancement via High Order Proximity Approximationhttps://www.ijcai.org/proceedings/2017/544
struc2vec: Learning Node Representations from Structural Identityhttps://arxiv.org/pdf/1704.03165
Poincaré Embeddings for Learning Hierarchical Representationshttps://arxiv.org/pdf/1705.08039
VERSE: Versatile Graph Embeddings from Similarity Measureshttps://arxiv.org/pdf/1803.04742
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vechttps://arxiv.org/pdf/1710.02971
Learning Structural Node Embeddings via Diffusion Waveletshttps://arxiv.org/pdf/1710.10321
Adversarial Network Embeddinghttps://arxiv.org/pdf/1711.07838
GraphGAN: Graph Representation Learning with Generative Adversarial Netshttps://arxiv.org/pdf/1711.08267
A General View for Network Embedding as Matrix Factorizationhttps://dl.acm.org/citation.cfm?id=3291029
Deep Graph Infomaxhttps://arxiv.org/pdf/1809.10341
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorizationhttp://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf
Adversarial Training Methods for Network Embeddinghttps://dl.acm.org/citation.cfm?id=3313445
vGraph: A Generative Model for Joint Community Detection and Node Representation Learninghttps://arxiv.org/pdf/1906.07159.pdf
ProGAN: Network Embedding via Proximity Generative Adversarial Networkhttps://dl.acm.org/citation.cfm?id=3330866
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embeddinghttps://openreview.net/pdf?id=r1lGO0EKDH
1.2   Node Representation Learning in Heterogeneous Graphshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id5
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#12node-representation-learning-in-heterogeneous-graphs
Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networkshttps://dl.acm.org/citation.cfm?id=2556225
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networkshttps://arxiv.org/pdf/1508.00200
Heterogeneous Network Embedding via Deep Architectureshttps://dl.acm.org/citation.cfm?id=2783296
Network Representation Learning with Rich Text Informationhttps://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098
Max-Margin DeepWalk: Discriminative Learning of Network Representationhttps://www.ijcai.org/Proceedings/16/Papers/547.pdf
metapath2vec: Scalable Representation Learning for Heterogeneous Networkshttps://dl.acm.org/citation.cfm?id=3098036
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networkshttps://arxiv.org/pdf/1610.09769
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learninghttps://dl.acm.org/citation.cfm?id=3132953
An Attention-based Collaboration Framework for Multi-View Network Representation Learninghttps://arxiv.org/pdf/1709.06636
Multi-view Clustering with Graph Embedding for Connectome Analysishttps://dl.acm.org/citation.cfm?id=3132909
Attributed Signed Network Embeddinghttps://dl.acm.org/citation.cfm?id=3132847.3132905
CANE: Context-Aware Network Embedding for Relation Modelinghttps://aclweb.org/anthology/papers/P/P17/P17-1158/
PME: Projected Metric Embedding on Heterogeneous Networks for Link Predictionhttps://dl.acm.org/citation.cfm?id=3219986
BiNE: Bipartite Network Embeddinghttps://dl.acm.org/citation.cfm?id=3209978.3209987
StarSpace: Embed All The Thingshttps://arxiv.org/pdf/1709.03856
Exploring Expert Cognition for Attributed Network Embeddinghttps://dl.acm.org/citation.cfm?id=3159655
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Predictionhttps://arxiv.org/pdf/1712.00732
Multidimensional Network Embedding with Hierarchical Structureshttps://dl.acm.org/citation.cfm?id=3159680
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learninghttps://dl.acm.org/citation.cfm?id=3159711
Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendationhttps://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be
ANRL: Attributed Network Representation Learning via Deep Neural Networkshttps://www.ijcai.org/proceedings/2018/438
Efficient Attributed Network Embedding via Recursive Randomized Hashinghttps://www.ijcai.org/proceedings/2018/397
Deep Attributed Network Embeddinghttps://www.ijcai.org/proceedings/2018/467
Co-Regularized Deep Multi-Network Embeddinghttps://dl.acm.org/citation.cfm?id=3186113
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networkshttps://arxiv.org/pdf/1807.03490
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insightshttps://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405
SIDE: Representation Learning in Signed Directed Networkshttps://dl.acm.org/citation.cfm?id=3186117
Learning Network-to-Network Model for Content-rich Network Embeddinghttps://dl.acm.org/citation.cfm?id=3330924
1.3   Node Representation Learning in Dynamic Graphshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id6
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#13node-representation-learning-in-dynamic-graphs
Know-evolve: Deep temporal reasoning for dynamic knowledge graphshttps://arxiv.org/pdf/1705.05742.pdf
Dyngem: Deep embedding method for dynamic graphshttps://arxiv.org/pdf/1805.11273.pdf
Attributed network embedding for learning in a dynamic environmenthttps://arxiv.org/pdf/1706.01860.pdf
Dynamic Network Embedding by Modeling Triadic Closure Processhttp://yangy.org/works/dynamictriad/dynamic_triad.pdf
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networkshttps://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869
TIMERS: Error-Bounded SVD Restart on Dynamic Networkshttps://arxiv.org/pdf/1711.09541.pdf
Dynamic Embeddings for User Profiling in Twitterhttps://dl.acm.org/citation.cfm?id=3219819.3220043
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embeddinghttps://www.ijcai.org/proceedings/2018/0288.pdf
DyRep: Learning Representations over Dynamic Graphshttps://openreview.net/pdf?id=HyePrhR5KX
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkshttps://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf
Variational Graph Recurrent Neural Networkshttps://arxiv.org/pdf/1908.09710.pdf
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkshttps://arxiv.org/pdf/1907.03395.pdf
2   Knowledge Graph Embeddinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id7
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#2knowledge-graph-embedding
A Three-Way Model for Collective Learning on Multi-Relational Data.http://www.icml-2011.org/papers/438_icmlpaper.pdf
Translating Embeddings for Modeling Multi-relational Datahttps://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf
Knowledge Graph Embedding by Translating on Hyperplaneshttps://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546
Reducing the Rank of Relational Factorization Models by Including Observable Patternshttp://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf
Learning Entity and Relation Embeddings for Knowledge Graph Completionhttps://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523
A Review of Relational Machine Learning for Knowledge Graphhttps://arxiv.org/pdf/1503.00759.pdf
Knowledge Graph Embedding via Dynamic Mapping Matrixhttps://www.aclweb.org/anthology/P15-1067
Modeling Relation Paths for Representation Learning of Knowledge Baseshttps://arxiv.org/pdf/1506.00379
Embedding Entities and Relations for Learning and Inference in Knowledge Baseshttps://arxiv.org/pdf/1412.6575
Holographic Embeddings of Knowledge Graphshttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828
Complex Embeddings for Simple Link Predictionhttp://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf
Modeling Relational Data with Graph Convolutional Networkshttps://arxiv.org/pdf/1703.06103
Fast Linear Model for Knowledge Graph Embeddingshttps://arxiv.org/pdf/1710.10881
Convolutional 2D Knowledge Graph Embeddingshttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884
Knowledge Graph Embedding With Iterative Guidance From Soft Ruleshttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011
KBGAN: Adversarial Learning for Knowledge Graph Embeddingshttps://arxiv.org/abs/1711.04071
Improving Knowledge Graph Embedding Using Simple Constraintshttps://arxiv.org/abs/1805.02408
SimplE Embedding for Link Prediction in Knowledge Graphshttps://arxiv.org/abs/1802.04868
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkhttps://aclweb.org/anthology/papers/N/N18/N18-2053/
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoninghttps://arxiv.org/abs/1903.08948
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacehttps://arxiv.org/abs/1902.10197
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphshttps://arxiv.org/abs/1906.01195
Probabilistic Logic Neural Networks for Reasoninghttps://arxiv.org/pdf/1906.08495.pdf
Quaternion Knowledge Graph Embeddingshttps://arxiv.org/pdf/1904.10281.pdf
Quantum Embedding of Knowledge for Reasoninghttps://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf
Multi-relational Poincaré Graph Embeddingshttps://arxiv.org/pdf/1905.09791.pdf
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoninghttps://openreview.net/forum?id=rkeuAhVKvB
3   Graph Neural Networkshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id8
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#3graph-neural-networks
Revisiting Semi-supervised Learning with Graph Embeddingshttps://arxiv.org/pdf/1603.08861
Semi-Supervised Classification with Graph Convolutional Networkshttps://arxiv.org/pdf/1609.02907
Neural Message Passing for Quantum Chemistryhttps://arxiv.org/pdf/1704.01212
Motif-Aware Graph Embeddingshttp://gearons.org/assets/docs/motif-aware-graph-final.pdf
Learning Graph Representations with Embedding Propagationhttps://arxiv.org/pdf/1710.03059
Inductive Representation Learning on Large Graphshttps://arxiv.org/pdf/1706.02216
Graph Attention Networkshttps://arxiv.org/pdf/1710.10903
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplinghttps://arxiv.org/pdf/1801.10247
Representation Learning on Graphs with Jumping Knowledge Networkshttps://arxiv.org/pdf/1806.03536
Stochastic Training of Graph Convolutional Networks with Variance Reductionhttps://arxiv.org/pdf/1710.10568
Large-Scale Learnable Graph Convolutional Networkshttps://arxiv.org/pdf/1808.03965
Adaptive Sampling Towards Fast Graph Representation Learninghttps://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf
Hierarchical Graph Representation Learning with Differentiable Poolinghttps://arxiv.org/pdf/1806.08804
Bayesian Semi-supervised Learning with Graph Gaussian Processeshttps://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf
Pitfalls of Graph Neural Network Evaluationhttps://arxiv.org/pdf/1811.05868
Heterogeneous Graph Attention Networkhttps://arxiv.org/pdf/1903.07293
Bayesian graph convolutional neural networks for semi-supervised classificationhttps://arxiv.org/pdf/1811.11103.pdf
How Powerful are Graph Neural Networks?https://arxiv.org/pdf/1810.00826
LanczosNet: Multi-Scale Deep Graph Convolutional Networkshttps://arxiv.org/pdf/1901.01484
Graph Wavelet Neural Networkhttps://arxiv.org/pdf/1904.07785
Supervised Community Detection with Line Graph Neural Networkshttps://openreview.net/pdf?id=H1g0Z3A9Fm
Predict then Propagate: Graph Neural Networks meet Personalized PageRankhttps://arxiv.org/pdf/1810.05997
Invariant and Equivariant Graph Networkshttps://arxiv.org/pdf/1812.09902
Capsule Graph Neural Networkhttps://openreview.net/pdf?id=Byl8BnRcYm
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixinghttps://arxiv.org/pdf/1905.00067
Graph U-Netshttps://arxiv.org/pdf/1905.05178
Disentangled Graph Convolutional Networkshttp://proceedings.mlr.press/v97/ma19a/ma19a.pdf
GMNN: Graph Markov Neural Networkshttps://arxiv.org/pdf/1905.06214
Simplifying Graph Convolutional Networkshttps://arxiv.org/pdf/1902.07153
Position-aware Graph Neural Networkshttps://arxiv.org/pdf/1906.04817
Self-Attention Graph Poolinghttps://arxiv.org/pdf/1904.08082
Relational Pooling for Graph Representationshttps://arxiv.org/pdf/1903.02541
Graph Representation Learning via Hard and Channel-Wise Attention Networkshttps://arxiv.org/pdf/1907.04652.pdf
Conditional Random Field Enhanced Graph Convolutional Neural Networkshttps://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkshttps://arxiv.org/abs/1905.07953
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationhttps://arxiv.org/abs/1906.02319
HetGNN: Heterogeneous Graph Neural Networkhttps://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network
Graph Recurrent Networks with Attributed Random Walkshttps://dl.acm.org/citation.cfm?id=3292500.3330941
Graph Convolutional Networks with EigenPoolinghttps://arxiv.org/abs/1904.13107
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filtershttp://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topologyhttps://arxiv.org/pdf/1907.05008.pdf
A Flexible Generative Framework for Graph-based Semi-supervised Learninghttps://arxiv.org/pdf/1905.10769.pdf
Rethinking Kernel Methods for Node Representation Learning on Graphshttps://arxiv.org/pdf/1910.02548.pdf
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networkshttps://arxiv.org/pdf/1906.02174.pdf
N-Gram Graph: A Simple Unsupervised Representation for Moleculeshttps://arxiv.org/pdf/1806.09206.pdf
DeepGCNs: Can GCNs Go as Deep as CNNs?https://arxiv.org/pdf/1904.03751.pdf
Continuous Graph Neural Networkshttps://arxiv.org/pdf/1912.00967.pdf
Curvature Graph Networkhttps://openreview.net/pdf?id=BylEqnVFDB
Memory-based Graph Networkshttps://openreview.net/pdf?id=r1laNeBYPB
Strategies for Pre-training Graph Neural Networkshttps://openreview.net/pdf?id=HJlWWJSFDH
4   Applications of Graph Deep Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id9
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#4applications-of-graph-deep-learning
4.1   Natural Language Processinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id10
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#41natural-language-processing
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelinghttps://www.aclweb.org/anthology/D17-1159
Graph Convolutional Encoders for Syntax-aware Neural Machine Translationhttps://www.aclweb.org/anthology/D17-1209
Graph-based Neural Multi-Document Summarizationhttps://www.aclweb.org/anthology/K17-1045
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehensionhttps://arxiv.org/pdf/1804.09541.pdf
A Structured Self-attentive Sentence Embeddinghttps://arxiv.org/pdf/1703.03130.pdf
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answeringhttps://aclweb.org/anthology/C18-1280
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networkshttps://www.aclweb.org/anthology/N18-2078
Linguistically-Informed Self-Attention for Semantic Role Labelinghttps://www.aclweb.org/anthology/D18-1548
Graph Convolution over Pruned Dependency Trees Improves Relation Extractionhttps://aclweb.org/anthology/D18-1244
A Graph-to-Sequence Model for AMR-to-Text Generationhttps://www.aclweb.org/anthology/P18-1150
Graph-to-Sequence Learning using Gated Graph Neural Networkshttps://www.aclweb.org/anthology/P18-1026
Graph Convolutional Networks for Text Classificationhttps://arxiv.org/pdf/1809.05679.pdf
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoderhttps://openreview.net/pdf?id=BJlgNh0qKQ
Structured Neural Summarizationhttps://arxiv.org/pdf/1811.01824.pdf
Multi-task Learning over Graph Structureshttps://arxiv.org/pdf/1811.10211.pdf
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typinghttps://arxiv.org/pdf/1903.02591.pdf
Single Document Summarization as Tree Inductionhttps://www.aclweb.org/anthology/N19-1173
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networkshttps://arxiv.org/pdf/1903.01306.pdf
Graph Neural Networks with Generated Parameters for Relation Extractionhttps://arxiv.org/pdf/1902.00756.pdf
Dynamically Fused Graph Network for Multi-hop Reasoninghttps://arxiv.org/pdf/1905.06933.pdf
Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Mediahttps://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf
Attention Guided Graph Convolutional Networks for Relation Extractionhttps://arxiv.org/pdf/1906.07510.pdf
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networkshttps://arxiv.org/pdf/1809.04283.pdf
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extractionhttps://tsujuifu.github.io/pubs/acl19_graph-rel.pdf
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphshttps://arxiv.org/pdf/1905.07374.pdf
Cognitive Graph for Multi-Hop Reading Comprehension at Scalehttps://arxiv.org/pdf/1905.05460.pdf
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Modelhttps://arxiv.org/pdf/1906.01231.pdf
Matching Article Pairs with Graphical Decomposition and Convolutionshttps://arxiv.org/pdf/1802.07459.pdf
Embedding Imputation with Grounded Language Informationhttps://arxiv.org/pdf/1906.03753.pdf
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Mediahttps://www.aclweb.org/anthology/P19-1247.pdf
A Neural Multi-digraph Model for Chinese NER with Gazetteershttps://www.aclweb.org/anthology/P19-1141.pdf
Tree Communication Models for Sentiment Analysishttps://www.aclweb.org/anthology/P19-1342.pdf
A2N: Attending to Neighbors for Knowledge Graph Inferencehttps://www.aclweb.org/anthology/P19-1431.pdf
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehensionhttps://www.aclweb.org/anthology/P19-1347.pdf
Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolutionhttps://arxiv.org/pdf/1905.08868.pdf
https://github.com/ianycxu/RGCN-with-BERThttps://github.com/ianycxu/RGCN-with-BERT
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representationshttps://arxiv.org/pdf/1901.06965.pdf
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarizationhttps://arxiv.org/pdf/1909.12231.pdf
Dependency-Guided LSTM-CRF for Named Entity Recognitionhttps://arxiv.org/pdf/1909.10148.pdf
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracityhttps://arxiv.org/pdf/1909.08211.pdf
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversationhttps://arxiv.org/pdf/1908.11540.pdf
Modeling Graph Structure in Transformer for Better AMR-to-Text Generationhttps://arxiv.org/pdf/1909.00136.pdf
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoninghttps://arxiv.org/pdf/1909.02151.pdf
4.2   Computer Visionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id11
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#42computer-vision
3D Graph Neural Networks for RGBD Semantic Segmentationhttp://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf
Situation Recognition With Graph Neural Networkshttps://arxiv.org/abs/1708.04320
Graph-Based Classification of Omnidirectional Imageshttps://arxiv.org/abs/1707.08301
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitionhttps://arxiv.org/abs/1801.07455
Image Generation from Scene Graphshttps://arxiv.org/abs/1804.01622
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformationhttps://arxiv.org/abs/1712.07262
PPFNet: Global Context Aware Local Features for Robust 3D Point Matchinghttps://arxiv.org/abs/1802.02669
Iterative Visual Reasoning Beyond Convolutionshttps://arxiv.org/abs/1803.11189
Surface Networkshttps://arxiv.org/abs/1705.10819
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysishttps://arxiv.org/abs/1706.05206
Learning to Act Properly: Predicting and Explaining Affordances From Imageshttps://arxiv.org/abs/1712.07576
Mining Point Cloud Local Structures by Kernel Correlation and Graph Poolinghttps://arxiv.org/abs/1712.06760
Deformable Shape Completion With Graph Convolutional Autoencodershttps://arxiv.org/abs/1712.00268
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imageshttps://arxiv.org/abs/1804.01654
Learning Human-Object Interactions by Graph Parsing Neural Networkshttps://arxiv.org/abs/1808.07962
Generating 3D Faces using Convolutional Mesh Autoencodershttps://arxiv.org/abs/1807.10267
Learning SO(3) Equivariant Representations with Spherical CNNshttps://arxiv.org/abs/1711.06721
Neural Graph Matching Networks for Fewshot 3D Action Recognitionhttp://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Cloudshttps://arxiv.org/abs/1809.05370
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Networkhttps://arxiv.org/abs/1906.00377
Graph R-CNN for Scene Graph Generationhttps://arxiv.org/abs/1808.00191
Exploring Visual Relationship for Image Captioninghttps://arxiv.org/abs/1809.07041
Beyond Grids: Learning Graph Representations for Visual Recognitionhttps://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition
Learning Conditioned Graph Structures for Interpretable Visual Question Answeringhttps://arxiv.org/abs/1806.07243
LinkNet: Relational Embedding for Scene Graphhttps://arxiv.org/abs/1811.06410
Flexible Neural Representation for Physics Predictionhttps://arxiv.org/abs/1806.08047
Learning Localized Generative Models for 3D Point Clouds via Graph Convolutionhttps://openreview.net/forum?id=SJeXSo09FQ
Graph-Based Global Reasoning Networkshttps://arxiv.org/abs/1811.12814
Deep Graph Laplacian Regularization for Robust Denoising of Real Imageshttps://arxiv.org/abs/1807.11637
Learning Context Graph for Person Searchhttps://arxiv.org/abs/1904.01830
Graphonomy: Universal Human Parsing via Graph Transfer Learninghttps://arxiv.org/abs/1904.04536
Masked Graph Attention Network for Person Re-Identificationhttp://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf
Learning to Cluster Faces on an Affinity Graphhttps://arxiv.org/abs/1904.02749
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognitionhttps://arxiv.org/abs/1904.12659
Adaptively Connected Neural Networkshttps://arxiv.org/abs/1904.03579
Reasoning Visual Dialogs with Structural and Partial Observationshttps://arxiv.org/abs/1904.03579
MeshCNN: A Network with an Edgehttps://arxiv.org/pdf/1809.05910.pdf
https://ranahanocka.github.io/MeshCNN/https://ranahanocka.github.io/MeshCNN/
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learninghttps://arxiv.org/pdf/1908.02441.pdf
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformationhttps://arxiv.org/pdf/1908.01491.pdf
Learning Trajectory Dependencies for Human Motion Predictionhttps://arxiv.org/pdf/1908.05436.pdf
Graph-Based Object Classification for Neuromorphic Vision Sensinghttps://arxiv.org/pdf/1908.06648.pdf
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramidhttps://arxiv.org/pdf/1908.11754.pdf
Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoninghttps://arxiv.org/pdf/1909.02144.pdf
Visual Semantic Reasoning for Image-Text Matchinghttps://arxiv.org/pdf/1909.02701.pdf
Graph Convolutional Networks for Temporal Action Localizationhttps://arxiv.org/pdf/1909.03252.pdf
Semantically-Regularized Logic Graph Embeddingshttps://arxiv.org/pdf/1909.01161.pdf
4.3   Recommender Systemshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id12
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#43recommender-systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systemshttps://arxiv.org/pdf/1806.01973.pdf
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendationhttps://arxiv.org/pdf/1811.02815.pdf
Session-based Social Recommendation via Dynamic Graph Attention Networkshttps://arxiv.org/pdf/1902.09362.pdf
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemshttps://arxiv.org/pdf/1903.10433.pdf
Graph Neural Networks for Social Recommendationhttps://arxiv.org/pdf/1902.07243.pdf
Session-based Recommendation with Graph Neural Networkshttps://arxiv.org/pdf/1811.00855.pdf
A Neural Influence Diffusion Model for Social Recommendationhttps://arxiv.org/pdf/1904.10322.pdf
Neural Graph Collaborative Filteringhttps://arxiv.org/pdf/1905.08108.pdf
Binarized Collaborative Filtering with Distilling Graph Convolutional Networkshttps://arxiv.org/pdf/1906.01829.pdf
IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendationhttps://dl.acm.org/citation.cfm?id=3330686
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendationhttps://arxiv.org/pdf/1908.04032.pdf
4.4   Link Predictionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id13
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#44link-prediction
Link Prediction Based on Graph Neural Networkshttps://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf
Link Prediction via Subgraph Embedding-Based Convex Matrix Completionhttp://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf
Graph Convolutional Matrix Completionhttps://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf
Semi-Implicit Graph Variational Auto-Encodershttps://arxiv.org/pdf/1908.07078.pdf
4.5   Influence Predictionhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id14
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#45influence-prediction
DeepInf: Social Influence Prediction with Deep Learninghttps://arxiv.org/pdf/1807.05560.pdf
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networkshttps://arxiv.org/pdf/1905.08865.pdf
4.6   Neural Architecture Searchhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id15
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#46neural-architecture-search
Graph HyperNetworks for Neural Architecture Searchhttps://openreview.net/pdf?id=rkgW0oA9FX
D-VAE: A Variational Autoencoder for Directed Acyclic Graphshttps://arxiv.org/pdf/1904.11088.pdf
4.7   Reinforcement Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id16
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#47reinforcement-learning
Action Schema Networks: Generalised Policies with Deep Learninghttps://arxiv.org/pdf/1709.04271.pdf
NerveNet: Learning Structured Policy with Graph Neural Networkshttps://openreview.net/pdf?id=S1sqHMZCb
Graph Networks as Learnable Physics Engines for Inference and Controlhttps://arxiv.org/pdf/1806.01242.pdf
Learning Policy Representations in Multiagent Systemshttps://arxiv.org/pdf/1806.06464.pdf
Relational recurrent neural networkshttps://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf
Transfer of Deep Reactive Policies for MDP Planninghttp://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf
Neural Graph Evolution: Towards Efficient Automatic Robot Designhttps://openreview.net/pdf?id=BkgWHnR5tm
No Press Diplomacy: Modeling Multi-Agent Gameplayhttps://arxiv.org/pdf/1909.02128.pdf
4.8   Combinatorial Optimizationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id17
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#48combinatorial-optimization
Learning Combinatorial Optimization Algorithms over Graphshttps://arxiv.org/abs/1704.01665
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Searchhttps://arxiv.org/abs/1810.10659
Reinforcement Learning for Solving the Vehicle Routing Problemhttps://arxiv.org/abs/1802.04240
Attention, Learn to Solve Routing Problems!https://arxiv.org/abs/1803.08475
Learning a SAT Solver from Single-Bit Supervisionhttps://arxiv.org/abs/1802.03685
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemhttps://arxiv.org/abs/1906.01227
Approximation Ratios of Graph Neural Networks for Combinatorial Problemshttps://arxiv.org/pdf/1905.10261.pdf
Exact Combinatorial Optimization with Graph Convolutional Neural Networkshttps://arxiv.org/pdf/1906.01629.pdf
On Learning Paradigms for the Travelling Salesman Problemhttps://arxiv.org/pdf/1910.07210.pdf
4.9   Adversarial Attack and Robustnesshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id18
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#49adversarial-attack-and-robustness
Adversarial Attack on Graph Structured Datahttps://arxiv.org/abs/1806.02371
Adversarial Attacks on Neural Networks for Graph Datahttps://arxiv.org/abs/1805.07984
Adversarial Attacks on Graph Neural Networks via Meta Learninghttps://arxiv.org/abs/1902.08412
Robust Graph Convolutional Networks Against Adversarial Attackshttp://pengcui.thumedialab.com/papers/RGCN.pdf
Certifiable Robustness and Robust Training for Graph Convolutional Networkshttps://arxiv.org/pdf/1906.12269.pdf
4.10   Graph Matchinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id19
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#410graph-matching
REGAL: Representation Learning-based Graph Alignmenthttps://arxiv.org/pdf/1802.06257.pdf
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networkshttps://www.aclweb.org/anthology/D18-1032.pdf
Learning Combinatorial Embedding Networks for Deep Graph Matchinghttp://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf
Deep Graph Matching Consensushttps://openreview.net/pdf?id=HyeJf1HKvS
4.11   Meta Learning and Few-shot Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id20
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#411meta-learning-and-few-shot-learning
Few-Shot Learning with Graph Neural Networkshttps://arxiv.org/abs/1711.04043
Learning Steady-States of Iterative Algorithms over Graphshttp://proceedings.mlr.press/v80/dai18a.html
Learning to Propagate for Graph Meta-Learninghttps://arxiv.org/pdf/1909.05024.pdf
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measureshttps://openreview.net/forum?id=Bkeeca4Kvr
Automated Relational Meta-learninghttps://openreview.net/pdf?id=rklp93EtwH
4.12   Structure Learninghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id21
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#412structure-learning
Neural Relational Inference for Interacting Systemshttps://arxiv.org/abs/1802.04687
Brain Signal Classification via Learning Connectivity Structurehttps://arxiv.org/abs/1905.11678
A Flexible Generative Framework for Graph-based Semi-supervised Learninghttps://arxiv.org/abs/1905.10769
Joint embedding of structure and features via graph convolutional networkshttps://arxiv.org/abs/1905.08636
Variational Spectral Graph Convolutional Networkshttps://arxiv.org/abs/1906.01852
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learninghttps://arxiv.org/abs/1805.10002
Graph Learning Network: A Structure Learning Algorithmhttps://arxiv.org/abs/1905.12665
Learning Discrete Structures for Graph Neural Networkshttps://arxiv.org/abs/1903.11960
Graphite: Iterative Generative Modeling of Graphshttps://arxiv.org/abs/1803.10459
4.13   Bioinformatics and Chemistryhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id22
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#413bioinformatics-and-chemistry
Protein Interface Prediction using Graph Convolutional Networkshttps://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf
Modeling Polypharmacy Side Effects with Graph Convolutional Networkshttps://arxiv.org/abs/1802.00543
NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactionshttps://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext
SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistryhttps://arxiv.org/pdf/1905.13741.pdf
Drug-Drug Adverse Effect Prediction with Graph Co-Attentionhttps://arxiv.org/pdf/1905.00534.pdf
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorizationhttps://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measureshttps://arxiv.org/pdf/1903.04571.pdf
PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networkshttps://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attentionhttps://ieeexplore.ieee.org/abstract/document/8665584
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorizationhttps://dl.acm.org/citation.cfm?id=3330912
Towards perturbation prediction of biological networks using deep learninghttps://www.nature.com/articles/s41598-019-48391-y
Directional Message Passing for Molecular Graphshttps://openreview.net/pdf?id=B1eWbxStPH
4.14   Graph Algorithmshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id23
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#414graph-algorithms
Neural Execution of Graph Algorithmshttps://openreview.net/pdf?id=SkgKO0EtvS
4.15   Theorem Provinghttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id24
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#415theorem-proving
Premise Selection for Theorem Proving by Deep Graph Embeddinghttps://arxiv.org/abs/1709.09994
5   Graph Generationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id25
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#5graph-generation
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelshttps://arxiv.org/abs/1802.08773
NetGAN: Generating Graphs via Random Walkshttps://arxiv.org/abs/1803.00816
Learning Deep Generative Models of Graphshttps://arxiv.org/abs/1803.03324
Junction Tree Variational Autoencoder for Molecular Graph Generationhttps://arxiv.org/abs/1802.04364
MolGAN: An implicit generative model for small molecular graphshttps://arxiv.org/abs/1805.11973
Generative Modeling for Protein Structureshttps://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencodershttps://arxiv.org/abs/1809.02630
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationhttps://arxiv.org/abs/1806.02473
Constrained Graph Variational Autoencoders for Molecule Designhttps://arxiv.org/abs/1805.09076
Learning Multimodal Graph-to-Graph Translation for Molecule Optimizationhttps://arxiv.org/abs/1812.01070
Generative Code Modeling with Graphshttps://openreview.net/forum?id=Bke4KsA5FX
DAG-GNN: DAG Structure Learning with Graph Neural Networkshttps://arxiv.org/abs/1904.10098
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generationhttp://proceedings.mlr.press/v89/sun19c.html
Graph Normalizing Flowshttps://arxiv.org/abs/1905.13177
Conditional Structure Generation through Graph Variational Generative Adversarial Netshttp://jiyang3.web.engr.illinois.edu/files/condgen.pdf
Efficient Graph Generation with Graph Recurrent Attention Networkshttps://arxiv.org/pdf/1910.00760.pdf
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationhttps://openreview.net/pdf?id=S1esMkHYPr
6   Graph Layout and High-dimensional Data Visualizationhttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id26
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#6graph-layout-and-high-dimensional-data-visualization
Visualizing Data using t-SNEhttp://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
Visualizing non-metric similarities in multiple mapshttps://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf
Visualizing Large-scale and High-dimensional Datahttps://arxiv.org/pdf/1602.00370
GraphTSNE: A Visualization Technique for Graph-Structured Datahttps://arxiv.org/pdf/1904.06915.pdf
7   Graph Representation Learning Systemshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id27
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#7graph-representation-learning-systems
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embeddinghttps://arxiv.org/pdf/1903.00757
PyTorch-BigGraph: A Large-scale Graph Embedding Systemhttps://arxiv.org/pdf/1903.12287
AliGraph: A Comprehensive Graph Neural Network Platformhttps://arxiv.org/pdf/1902.08730
Deep Graph Libraryhttps://www.dgl.ai
AmpliGraphhttps://github.com/Accenture/AmpliGraph
Eulerhttps://github.com/alibaba/euler
8   Datasetshttps://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#id28
https://patch-diff.githubusercontent.com/DeepGraphLearning/LiteratureDL4Graph#8datasets
ATOMIC: an atlas of machine commonsense for if-then reasoninghttps://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038
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