A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.
Authors
Zhiyuan Liu
34 papers
Maosong Sun
46 papers
Jie Zhou
14 papers
Ganqu Cui
5 papers
Cheng Yang
4 papers
Zhengyan Zhang
3 papers
References302 items
1
Design Space for Graph Neural Networks
2
Graph Embedding for Combinatorial Optimization: A Survey
3
Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks
4
Adaptive Graph Encoder for Attributed Graph Embedding
5
Scaling Graph Neural Networks with Approximate PageRank
6
F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification
7
GPT-GNN: Generative Pre-Training of Graph Neural Networks
8
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
9
Graph Policy Network for Transferable Active Learning on Graphs
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Contrastive Multi-View Representation Learning on Graphs
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Graphon Neural Networks and the Transferability of Graph Neural Networks
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy
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Deep Lagrangian Constraint-based Propagation in Graph Neural Networks
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
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SIGN: Scalable Inception Graph Neural Networks
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Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection
17
Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond
18
A Survey of Adversarial Learning on Graphs
19
Heterogeneous Graph Transformer
20
Unifying Graph Convolutional Neural Networks and Label Propagation
21
Generalization and Representational Limits of Graph Neural Networks
22
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
23
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
24
Graph-Bert: Only Attention is Needed for Learning Graph Representations
25
A Fair Comparison of Graph Neural Networks for Graph Classification
26
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
27
GMAN: A Graph Multi-Attention Network for Traffic Prediction
28
Graph convolutional networks: a comprehensive review
29
Graph Transformer Networks
30
Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks
31
Fine-grained Fact Verification with Kernel Graph Attention Network
32
Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis
33
Reasoning Over Semantic-Level Graph for Fact Checking
34
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
35
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
36
Learning Sparse Nonparametric DAGs
37
Neural Dynamics on Complex Networks
38
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
39
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
40
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
41
Transferability of Spectral Graph Convolutional Neural Networks
Deep Neural Networks for Learning Graph Representations
234
Long Short-Term Memory-Networks for Machine Reading
235
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
236
Deep Residual Learning for Image Recognition
237
Order Matters: Sequence to sequence for sets
238
Gated Graph Sequence Neural Networks
239
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
240
Diffusion-Convolutional Neural Networks
241
Convolutional Networks on Graphs for Learning Molecular Fingerprints
242
Network Representation Learning with Rich Text Information
243
Deep Convolutional Networks on Graph-Structured Data
244
Pointer Networks
245
LINE: Large-scale Information Network Embedding
246
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
247
Geodesic Convolutional Neural Networks on Riemannian Manifolds
248
Neural Machine Translation by Jointly Learning to Align and Translate
249
ImageNet Large Scale Visual Recognition Challenge
250
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
251
DeepWalk: online learning of social representations
252
Spectral Networks and Locally Connected Networks on Graphs
253
Reasoning With Neural Tensor Networks for Knowledge Base Completion
254
Translating Embeddings for Modeling Multi-relational Data
255
Efficient Estimation of Word Representations in Vector Space
256
ImageNet classification with deep convolutional neural networks
257
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
258
PathSim
259
Graph Echo State Networks
260
Wavelets on Graphs via Spectral Graph Theory
261
Neural Network for Graphs: A Contextual Constructive Approach
262
Et al
263
ArnetMiner: extraction and mining of academic social networks
264
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
265
Comparison of descriptor spaces for chemical compound retrieval and classification
266
A new model for learning in graph domains
267
A non-local algorithm for image denoising
268
Contextual processing of structured data by recursive cascade correlation
269
Recursive self-organizing network models
270
Distinguishing enzyme structures from non-enzymes without alignments.
271
Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001
272
An Introduction to Metric Spaces and Fixed Point Theory
273
A general framework for adaptive processing of data structures
274
Bilateral filtering for gray and color images
275
Long Short-Term Memory
276
Supervised neural networks for the classification of structures
277
Spectral Graph Theory
278
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity.
279
A Comprehensive Survey on Graph Neural Networks
280
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
281
Logical Expressiveness of Graph Neural Networks
282
Structure-Aware Convolutional Neural Networks
283
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
284
As the research filed grows rapidly, we recommend our readers the paper list published by our team, GNNPapers (https://github.com/thunlp/ gnnpapers), for recent papers
285
“GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models”
286
UvA-DARE ( Digital Academic Repository ) Graph Convolutional Matrix Completion
287
Deep Learning
288
Representing Text for Joint Embedding of Text and Knowledge Bases
289
Computational Capabilities of Graph Neural Networks
290
The Graph Neural Network Model
291
Social Computing Data Repository at ASU. http://soci alcomputing.asu.edu
292
Protein function prediction via graph kernels
293
The''echo state''approach to analysing and training recurrent neural networks
294
Statistical evaluation of the predictive toxicology challenge
295
Gradient-based learning applied to document recognition
296
A wavelet tour of signal processing
297
Reinforcement Learning: An Introduction
298
Finite Geometrical Systems: Six Public Lectues Delivered in February
299
Finite Geometrical Systems: Six Public Lectues Delivered in February, 1940. the University of Calcutta, University of Calcutta
300
Add more models and applications based on recent papers and update figures and references
301
Add two coauthors and we thank them for their kindly suggestions and contributions