Graph Neural Networks: A Review of Methods and Applications

Abstract

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Published at AI Open 2021

References in corpus (36)

• Deep Convolutional Networks on Graph-Structured Data
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
• Fast Graph Representation Learning with PyTorch Geometric
• Simplifying Graph Convolutional Networks
• Graph Convolutional Matrix Completion
• Variational Graph Auto-Encoders
• Interaction Networks for Learning about Objects, Relations and Physics
• Predicting multicellular function through multi-layer tissue networks
• Graph Transformer Networks
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• Adversarial Attack and Defense on Graph Data: A Survey
• Neural Combinatorial Optimization with Reinforcement Learning
• Exact Combinatorial Optimization with Graph Convolutional Neural Networks
• Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
• Strategies for Pre-training Graph Neural Networks
• A Compositional Object-Based Approach to Learning Physical Dynamics
• Universal Invariant and Equivariant Graph Neural Networks
• HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
• MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
• A Fair Comparison of Graph Neural Networks for Graph Classification
• GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• Unifying Graph Convolutional Neural Networks and Label Propagation
• Invariant and Equivariant Graph Networks
• Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
• Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
• Discovering objects and their relations from entangled scene representations
• Transferability of Spectral Graph Convolutional Neural Networks
• Explainability Techniques for Graph Convolutional Networks
• Active Learning for Graph Embedding
• Dynamically Fused Graph Network for Multi-hop Reasoning
• Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis
• Interpretable Structure-Evolving LSTM

Cited by in corpus (340)

• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
• Graph Neural Network for Traffic Forecasting: A Survey
• Simplifying Graph Convolutional Networks
• A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
• E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
• Self-Attention Graph Pooling
• Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
• TUDataset: A collection of benchmark datasets for learning with graphs
• How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
• An Overview of Deep Semi-Supervised Learning
• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
• Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
• DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
• DeepGCNs: Making GCNs Go as Deep as CNNs
• Metrics for Graph Comparison: A Practitioner's Guide
• KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
• Deep Learning on Graphs: A Survey
• Graph Neural Networks: Taxonomy, Advances and Trends
• HDMI: High-order Deep Multiplex Infomax
• Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
• Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
• Universal Invariant and Equivariant Graph Neural Networks
• Graph Convolutional Networks for Graphs Containing Missing Features
• Hierarchical Graph Pooling with Structure Learning
• How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
• RouteNet-Fermi: Network Modeling with Graph Neural Networks
• DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
• Self-supervised Learning on Graphs: Deep Insights and New Direction
• Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• Deep learning of thermodynamics-aware reduced-order models from data
• A Note on Over-Smoothing for Graph Neural Networks
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• A Survey on The Expressive Power of Graph Neural Networks
• Towards Deeper Graph Neural Networks with Differentiable Group Normalization
• Kriging Convolutional Networks
• Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
• K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
• Residual Correlation in Graph Neural Network Regression
• Measuring and Improving the Use of Graph Information in Graph Neural Networks
• Automated Machine Learning on Graphs: A Survey
• Representation Learning for Dynamic Graphs: A Survey
• Accurate Learning of Graph Representations with Graph Multiset Pooling
• Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
• Graph Kernels: State-of-the-Art and Future Challenges
• Trans4E: Link Prediction on Scholarly Knowledge Graphs
• DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• Implicit Graph Neural Networks
• Multilayer Network Analysis for Improved Credit Risk Prediction
• Uncertainty Aware Semi-Supervised Learning on Graph Data
• Towards Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses
• Graph Structure Learning for Robust Graph Neural Networks
• Relational Deep Feature Learning for Heterogeneous Face Recognition
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
• MagNet: A Neural Network for Directed Graphs
• Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrolling
• GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
• Automating Botnet Detection with Graph Neural Networks
• Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
• Subgraph Neural Networks
• AEGCN: An Autoencoder-Constrained Graph Convolutional Network
• Continuous Graph Neural Networks
• Neural ranking models for document retrieval
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
• Hypergraph Convolution and Hypergraph Attention
• GINNs: Graph-Informed Neural Networks for Multiscale Physics
• Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks
• Graph neural network-based fault diagnosis: a review
• Cell Complex Neural Networks
• Dynamic Graph Convolutional Networks Using the Tensor M-Product
• Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
• Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
• Path Integral Based Convolution and Pooling for Graph Neural Networks
• Graph Star Net for Generalized Multi-Task Learning
• Incorporating Symbolic Domain Knowledge into Graph Neural Networks
• Edge-Featured Graph Attention Network
• Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
• Discrete and continuous representations and processing in deep learning: Looking forward
• Can Graph Neural Networks Count Substructures?
• Interpreting and Unifying Graph Neural Networks with An Optimization Framework
• Representation, learning, and planning algorithms for geometric task and motion planning
• A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
• Language-Conditioned Graph Networks for Relational Reasoning
• A Unified Lottery Ticket Hypothesis for Graph Neural Networks
• A Review of Graph Neural Networks and Their Applications in Power Systems
• Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
• Probing triple Higgs coupling with machine learning at the LHC
• Solving the single-track train scheduling problem via Deep Reinforcement Learning
• Bayesian graph convolutional neural networks via tempered MCMC
• Understanding and Resolving Performance Degradation in Graph Convolutional Networks
• Reliable Graph Neural Networks via Robust Aggregation
• Graph Policy Network for Transferable Active Learning on Graphs
• A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
• Hybrid Quantum-Classical Graph Convolutional Network
• Deep Learning for Scene Classification: A Survey
• Understanding the Role of Scene Graphs in Visual Question Answering
• Structured Citation Trend Prediction Using Graph Neural Networks
• Building powerful and equivariant graph neural networks with structural message-passing
• A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
• Graph Element Networks: adaptive, structured computation and memory
• An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
• Graph Condensation for Graph Neural Networks
• Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion
• Structured Object-Aware Physics Prediction for Video Modeling and Planning
• Explaining Deep Graph Networks with Molecular Counterfactuals
• Edge Representation Learning with Hypergraphs
• Beyond Graph Neural Networks with Lifted Relational Neural Networks
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Deep Learning in Protein Structural Modeling and Design
• AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
• Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
• Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
• Evaluating Deep Graph Neural Networks
• Multi-Label Image Classification with Contrastive Learning
• Decoding Dark Matter Substructure without Supervision
• Learning to Drop: Robust Graph Neural Network via Topological Denoising
• Graph Neural Networks for reconstruction and classification in KM3NeT
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
• SWAG: Item Recommendations using Convolutions on Weighted Graphs
• Constructions in combinatorics via neural networks
• Incomplete Graph Representation and Learning via Partial Graph Neural Networks
• Insights from Graph Theory on the Morphologies of Actomyosin Networks with Multilinkers
• Learning Cross-Domain Representation with Multi-Graph Neural Network
• GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
• HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
• Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
• Categorical Normalizing Flows via Continuous Transformations
• Node Similarity Preserving Graph Convolutional Networks
• DRIFT: Deep Reinforcement Learning for Functional Software Testing
• NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
• Active Learning of Abstract Plan Feasibility
• Meta-Learning with Graph Neural Networks: Methods and Applications
• Higher-order Weighted Graph Convolutional Networks
• Graph Neural Networks Inspired by Classical Iterative Algorithms
• Track Seeding and Labelling with Embedded-space Graph Neural Networks
• Learning to simulate and design for structural engineering
• Deep Graph Similarity Learning: A Survey
• Text Level Graph Neural Network for Text Classification
• HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
• Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
• A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks
• Recursive Graphical Neural Networks for Text Classification
• Memory-Based Graph Networks
• Contrastive Graph Neural Network Explanation
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
• Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph
• Graph Generative Models for Fast Detector Simulations in High Energy Physics
• RepGN:Object Detection with Relational Proposal Graph Network
• GraphConfRec: A Graph Neural Network-Based Conference Recommender System
• Build2Vec: Building Representation in Vector Space
• Learning the Relation between Code Features and Code Transforms with Structured Prediction
• Multi-task Batch Reinforcement Learning with Metric Learning
• Optimization of Graph Neural Networks with Natural Gradient Descent
• DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
• Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
• ActiveHNE: Active Heterogeneous Network Embedding
• Rubik: A Hierarchical Architecture for Efficient Graph Learning
• Graph Convolutional Networks with EigenPooling
• EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
• Transient Chaos in BERT
• Lifelong Graph Learning
• CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
• Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
• Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
• flexgrid2vec: Learning Efficient Visual Representations Vectors
• Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
• Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
• Relational Graph Representation Learning for Open-Domain Question Answering
• A Hierarchy of Graph Neural Networks Based on Learnable Local Features
• Tiered Latent Representations and Latent Spaces for Molecular Graphs
• Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
• Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery
• Message Passing Neural Processes
• Utilising Graph Machine Learning within Drug Discovery and Development
• Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision
• Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
• Categorical Representation Learning: Morphism is All You Need
• Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision
• EPNE: Evolutionary Pattern Preserving Network Embedding
• Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
• EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
• Graph convolutions that can finally model local structure
• High-resolution rainfall-runoff modeling using graph neural network
• Policy-GNN: Aggregation Optimization for Graph Neural Networks
• Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
• Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
• Natural Language QA Approaches using Reasoning with External Knowledge
• Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation
• Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation
• Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks
• SMASH: Sparse Matrix Atomic Scratchpad Hashing
• Neural Packet Classification
• Scaling Graph-based Deep Learning models to larger networks
• Quaternion Graph Neural Networks
• Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
• Distributionally Robust Semi-Supervised Learning Over Graphs
• Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approach
• Dynamic Graph Representation for Partially Occluded Biometrics
• Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
• GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations
• Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
• Graph Neural Processes: Towards Bayesian Graph Neural Networks
• Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
• A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
• Generalization bounds for graph convolutional neural networks via Rademacher complexity
• Spectral Temporal Graph Neural Network for Trajectory Prediction
• Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
• Scalable Attack on Graph Data by Injecting Vicious Nodes
• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
• Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
• Dynamic Spatiotemporal Graph Neural Network with Tensor Network
• New Ideas and Trends in Deep Multimodal Content Understanding: A Review
• Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
• GraphTTS: graph-to-sequence modelling in neural text-to-speech
• PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
• The Atlas for the Aspiring Network Scientist
• Fast Training of Sparse Graph Neural Networks on Dense Hardware
• Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook
• GraLSP: Graph Neural Networks with Local Structural Patterns
• FedGraph: Federated Graph Learning with Intelligent Sampling
• Multimodal and Contrastive Learning for Click Fraud Detection
• Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
• A Framework for End-to-End Learning on Semantic Tree-Structured Data
• Adversarial methods to reduce simulation bias in neutrino interaction event filtering at Liquid Argon Time Projection Chambers
• SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
• Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions
• A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks
• CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries
• Machine Learning for Robotic Manipulation
• Towards a predictive spatio-temporal representation of brain data
• Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
• GmCN: Graph Mask Convolutional Network
• Learning with Molecules beyond Graph Neural Networks
• Deep Generative Modeling in Network Science with Applications to Public Policy Research
• GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
• Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features
• Temporal Reasoning Graph for Activity Recognition
• Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility
• Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook
• Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information
• AppQ: Warm-starting App Recommendation Based on View Graphs
• Graph Belief Propagation Networks
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
• Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
• Recurrent Attention Walk for Semi-supervised Classification
• Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks
• When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
• Structure-Aware Label Smoothing for Graph Neural Networks
• SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction
• Inferring halo masses with Graph Neural Networks
• Learning Robust Representations with Graph Denoising Policy Network
• Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
• Graph Neural Networks for Node-Level Predictions
• Particle Track Reconstruction using Geometric Deep Learning
• Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
• ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks
• Structure-Aware Human-Action Generation
• A Unified Framework for Cross-Domain and Cross-System Recommendations
• Data-Driven Construction of Data Center Graph of Things for Anomaly Detection
• Neural Topic Modeling by Incorporating Document Relationship Graph
• InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
• Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene
• Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
• Neural Stochastic Block Model & Scalable Community-Based Graph Learning
• AHINE: Adaptive Heterogeneous Information Network Embedding
• Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
• Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective
• End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks
• Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution
• Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
• Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center
• Deep Multi-attribute Graph Representation Learning on Protein Structures
• Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
• Approximate Fixed-Points in Recurrent Neural Networks
• Context-Aware Graph Attention Networks
• Embedding Symbolic Temporal Knowledge into Deep Sequential Models
• UCNN: A Convolutional Strategy on Unstructured Mesh
• Towards Robust Pattern Recognition: A Review
• GCCN: Global Context Convolutional Network
• Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
• Implicit vs Unfolded Graph Neural Networks
• Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
• A Graph Neural Network Approach for Product Relationship Prediction
• Graph Feature Gating Networks
• Learning Fine-Grained Segmentation of 3D Shapes without Part Labels
• Automated Graph Learning via Population Based Self-Tuning GCN
• PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention
• Graph Convolutional Network with Generalized Factorized Bilinear Aggregation
• Training Graph Neural Networks by Graphon Estimation
• Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder
• Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
• Co-embedding of Nodes and Edges with Graph Neural Networks
• Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
• Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs
• A Weighted Heterogeneous Graph Based Dialogue System
• Learning Graph Neural Networks with Approximate Gradient Descent
• Automatic design of novel potential 3CL${}^{\text{pro}}$ and PL${}^{\text{pro}}$ inhibitors
• ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening
• Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
• Deep Graph Matching under Quadratic Constraint
• Graph Convolutional Network for Swahili News Classification
• Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
• Applications of Common Information to Computing Functions
• Deep Clustering with Measure Propagation
• Integrated Node Encoder for Labelled Textual Networks
• Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
• Relevant Region Prediction for Crowd Counting
• Graph Neural Network for Hamiltonian-Based Material Property Prediction
• Inducing Optimal Attribute Representations for Conditional GANs
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
• Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
• Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
• Learn to Propagate Reliably on Noisy Affinity Graphs
• Brushing Feature Values in Immersive Graph Visualization Environment
• A Metamodel and Framework for AGI
• DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
• CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
• Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing
• CAGE: Context-Aware Grasping Engine
• Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
• Fast Haar Transforms for Graph Neural Networks
• DeepDrawing: A Deep Learning Approach to Graph Drawing
• Can NetGAN be improved on short random walks?
• Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
• A New Clustering neural network for Chinese word segmentation
• Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering