README.md

Must read papers on continuous track on Graph Neural Network progress

• Graph Convolutional Network Hashing for Cross-Modal Retrieval
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning
• Dynamic Hypergraph Neural Networks
• ANRL: Attributed Network Representation Learning via Deep Neural Networks
• Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks
• Adversarial Graph Embedding for Ensemble Clustering
• Graph Contextualized Self-Attention Network for Session-based Recommendation
• Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology
• Masked Graph Convolutional Network
• AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN
• Deep Convolutional Networks on Graph-Structured Data
• Gated Graph Sequence Neural Networks
• Robust Spatial Filtering with Graph Convolutional Neural Networks
• Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
• A Generalization of Convolutional Neural Networks to Graph-Structured Data
• CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
• Topology Adaptive Graph Convolutional Networks
• Graph Attention Networks
• Residual Gated Graph ConvNets
• DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model
• Dynamic Graph CNN for Learning on Point Clouds
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• GeniePath: Graph Neural Networks with Adaptive Receptive Paths
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
• Improving Graph Convolutional Networks with Non-Parametric Activation Functions
• Link Prediction Based on Graph Neural Networks
• Tensor graph convolutional neural network
• Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
• Semi-supervised User Geolocation via Graph Convolutional Networks
• Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
• Adversarial Attacks on Neural Networks for Graph Data
• Graph Capsule Convolutional Neural Networks
• Deep Graph Translation
• Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
• Rethinking Knowledge Graph Propagation for Zero-Shot Learning
• Relational inductive biases, deep learning, and graph networks
• Graph-to-Sequence Learning using Gated Graph Neural Networks
• When Work Matters: Transforming Classical Network Structures to Graph CNN
• Large-Scale Learnable Graph Convolutional Networks
• SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
• Topology and Prediction Focused Research on Graph Convolutional Neural Networks
• Adversarial Attacks on Node Embeddings via Graph Poisoning
• Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP
• Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting
• Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
• Graph Convolutional Networks for Text Classification
• Graph Neural Networks for IceCube Signal Classification
• Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
• Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
• How Powerful are Graph Neural Networks?
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank
• Towards Efficient Large-Scale Graph Neural Network Computing
• Session-based Recommendation with Graph Neural Networks
• Pitfalls of Graph Neural Network Evaluation
• Deep Learning on Graphs: A Survey
• Graph Neural Networks: A Review of Methods and Applications
• Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
• A Comprehensive Survey on Graph Neural Networks
• Learning Graph Embedding with Adversarial Training Methods
• LanczosNet: Multi-Scale Deep Graph Convolutional Networks
• All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
• Graph Neural Networks with Generated Parameters for Relation Extraction
• Context-Aware Visual Compatibility Prediction
• Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
• Simplifying Graph Convolutional Networks
• Graph Neural Networks for Social Recommendation
• Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
• Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations
• An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
• Batch Virtual Adversarial Training for Graph Convolutional Networks
• Graph Neural Networks for Modelling Traffic Participant Interaction
• Gated Graph Convolutional Recurrent Neural Networks
• Graph Neural Networks for User Identity Linkage
• Fast Graph Representation Learning with PyTorch Geometric
• Relational Pooling for Graph Representations
• Fisher-Bures Adversary Graph Convolutional Networks
• InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
• Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
• Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
• An End-to-End Network for Generating Social Relationship Graphs
• Linkage Based Face Clustering via Graph Convolution Network
• PyTorch-BigGraph: A Large-scale Graph Embedding System
• Relation-Aware Graph Attention Network for Visual Question Answering
• Invariance-Preserving Localized Activation Functions for Graph Neural Networks
• Learning Combinatorial Embedding Networks for Deep Graph Matching
• Learning Context Graph for Person Search
• Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
• Learning to Cluster Faces on an Affinity Graph
• Multi-Label Image Recognition with Graph Convolutional Networks
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• Self-Attention Graph Pooling
• Graph Element Networks: adaptive, structured computation and memory
• DAG-GNN: DAG Structure Learning with Graph Neural Networks
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• Graph Convolutional Networks with EigenPooling
• Edge-Labeling Graph Neural Network for Few-shot Learning
• PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
• Graph U-Nets
• Graph Convolutional Gaussian Processes
• GMNN: Graph Markov Neural Networks
• Function Space Pooling For Graph Convolutional Networks
• 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
• ncRNA Classification with Graph Convolutional Networks
• Inferring Javascript types using Graph Neural Networks
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
• Joint embedding of structure and features via graph convolutional networks
• Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
• Graph Representations for Higher-Order Logic and Theorem Proving
• Attributed Graph Clustering via Adaptive Graph Convolution
• Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
• iPool - Information-based Pooling in Hierarchical Graph Neural Networks
• Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph
• Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
• GraphSAINT: Graph Sampling Based Inductive Learning Method
• Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
• Graph-Based Object Classification for Neuromorphic Vision Sensing
• Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
• Auto-GNN: Neural Architecture Search of Graph Neural Networks
• Graph Convolutional Networks for Temporal Action Localization
• Universal Self-Attention Network for Graph Classification
• Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
• Fast and Deep Graph Neural Networks
• Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
• Multi-Component Graph Convolutional Collaborative Filtering
• Effective Decoding in Graph Auto-Encoder Using Triadic Closure
• Independence Promoted Graph Disentangled Networks
• STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting
• Continuous Graph Neural Networks
• Grid-GCN for Fast and Scalable Point Cloud Learning
• Coloring graph neural networks for node disambiguation
• TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
• Structural Deep Clustering Network
• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
• Bilinear Graph Neural Network with Neighbor Interactions
• Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
• Benchmarking Graph Neural Networks
• Learning to Hash with Graph Neural Networks for Recommender Systems
• VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
• Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
• Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
• Understanding Negative Sampling in Graph Representation Learning
• M2GRL: A Multi-task Multi-view Graph Representation Learning Framework forWeb-scale Recommender Systems
• Convolutional Networks on Graphs for Learning Molecular Fingerprints
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
• Protein Interface Prediction using Graph Convolutional Networks
• Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
• Conditional Structure Generation through Graph Variational Generative Adversarial Nets
• HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
• Provably Powerful Graph Networks
• Hyper-Graph-Network Decoders for Block Codes
• Learning Transferable Graph Exploration
• Approximation Ratios of Graph Neural Networks for Combinatorial Problems
• Understanding Attention and Generalization in Graph Neural Networks
• Efficient Graph Generation with Graph Recurrent Attention Networks
• End to end learning and optimization on graphs
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
• DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
• Hyperbolic Graph Neural Networks
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
• Variational Graph Recurrent Neural Networks
• Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
• Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
• Graph Transformer Networks
• Recurrent Space-time Graph Neural Networks
• Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
• Exact Combinatorial Optimization with Graph Convolutional Neural Networks
• On the equivalence between graph isomorphism testing and function approximation with GNNs
• A Fair Comparison of Graph Neural Networks for Graph Classification
• Adaptive Structural Fingerprints for Graph Attention Networks
• Adversarial Attacks on Graph Neural Networks via Meta Learning
• A graph-convolutional neural network model for the prediction of chemical reactivity
• Capsule Graph Neural Network
• Stochastic Training of Graph Convolutional Networks with Variance Reduction
• Composition-based Multi-Relational Graph Convolutional Networks
• Curvature Graph Network
• Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
• Modeling Polypharmacy Side Effects with Graph Convolutional Networks
• Efficient Probabilistic Logic Reasoning with Graph Neural Networks
• Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
• Geom-GCN: Geometric Graph Convolutional Networks
• Graph Convolutional Reinforcement Learning
• Graph HyperNetworks for Neural Architecture Search
• Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
• Graph Wavelet Neural Network
• Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
• Hyperbolic Attention Networks
• A Persistent Weisfeiler–Lehman Procedure for Graph Classification
• Inductive Matrix Completion Based on Graph Neural Networks
• Invariant and Equivariant Graph Networks
• Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
• LambdaNet: Probabilistic Type Inference using Graph Neural Networks
• Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media
• Measuring and Improving the Use of Graph Information in Graph Neural Networks
• Memory-Based Graph Networks
• BrainNetCNN: Convolutional Neural Networks for Brain Networks; Towards Predicting Neurodevelopment
• Learning Convolutional Neural Networks for Graphs
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Next-Generation Machine Learning for Biological Networks
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning
• Machine learning meets complex networks via coalescent embedding in the hyperbolic space
• Graph Networks as Learnable Physics Engines for Inference and Control
• The graph neural network model
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
• Metric Learning with Spectral Graph Convolutions on Brain Connectivity Networks
• Strategies for Pre-training Graph Neural Networks
• Semi-Supervised Classification with Graph Convolutional Networks
• Supervised Community Detection with Line Graph Neural Networks
• The Logical Expressiveness of Graph Neural Networks
• Deep Learning of Graphs with Ngram Convolutional Neural Networks
• What graph neural networks cannot learn: depth vs width
• Unsupervised Domain Adaptive Graph Convolutional Networks
• Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
• Circuit-GNN: Graph Neural Networks for Distributed Circuit Design