Inductive Representation Learning on Large Graphs

Abstract

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Published in NIPS 2017; version with full appendix and minor corrections

References in corpus (2)

• Variational Graph Auto-Encoders
• Stochastic Training of Graph Convolutional Networks with Variance Reduction

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• Hyperbolic Graph Attention Network
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
• Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
• Energy-based View of Retrosynthesis
• Learning to Drop: Robust Graph Neural Network via Topological Denoising
• DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
• Beltrami Flow and Neural Diffusion on Graphs
• Graph Neural Networks for IceCube Signal Classification
• Knowledge Graph Enhanced Event Extraction in Financial Documents
• Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
• Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
• Incomplete Graph Representation and Learning via Partial Graph Neural Networks
• Single-Node Attacks for Fooling Graph Neural Networks
• Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
• Bilinear Graph Neural Network with Neighbor Interactions
• Learning Cross-Domain Representation with Multi-Graph Neural Network
• V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
• Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
• SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
• Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
• Large-scale graph representation learning with very deep GNNs and self-supervision
• HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
• Evolutionary Architecture Search for Graph Neural Networks
• Explainability-based Backdoor Attacks Against Graph Neural Networks
• GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
• Bayesian Graph Convolutional Neural Networks using Node Copying
• VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs
• RNAglib: A Python Package for RNA 2.5D Graphs
• Deep Graph Neural Networks with Shallow Subgraph Samplers
• Graph-Based Machine Learning Improves Just-in-Time Defect Prediction
• Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
• Equivariant Subgraph Aggregation Networks
• Sampling methods for efficient training of graph convolutional networks: A survey
• TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
• NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
• FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
• Node Similarity Preserving Graph Convolutional Networks
• Graph Neural Networks Inspired by Classical Iterative Algorithms
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
• PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
• Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
• Text Level Graph Neural Network for Text Classification
• FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
• Multi-dimensional Graph Convolutional Networks
• iPool -- Information-based Pooling in Hierarchical Graph Neural Networks
• FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
• Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
• Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
• Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
• Zero-Shot Learning with Common Sense Knowledge Graphs
• Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
• SGAS: Sequential Greedy Architecture Search
• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
• Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
• Stability and Generalization of Graph Convolutional Neural Networks
• Sequential Scenario-Specific Meta Learner for Online Recommendation
• CasGCN: Predicting future cascade growth based on information diffusion graph
• A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
• Benchmarks for Graph Embedding Evaluation
• Contrastive Graph Neural Network Explanation
• LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
• Graph Generative Models for Fast Detector Simulations in High Energy Physics
• Distinguish Confusing Law Articles for Legal Judgment Prediction
• Accurate, Efficient and Scalable Training of Graph Neural Networks
• On Graph Classification Networks, Datasets and Baselines
• Android Malware Detection using Large-scale Network Representation Learning
• Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
• Fisher-Bures Adversary Graph Convolutional Networks
• Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
• FedGL: Federated Graph Learning Framework with Global Self-Supervision
• Graph Meta Learning via Local Subgraphs
• Global Attention Improves Graph Networks Generalization
• Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
• Graph Transformation Policy Network for Chemical Reaction Prediction
• SPAGAN: Shortest Path Graph Attention Network
• Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
• Signed Graph Diffusion Network
• CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling
• STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
• Heterogeneous Graph Attention Network
• edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
• Reinforced Epidemic Control: Saving Both Lives and Economy
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
• Discriminative structural graph classification
• Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
• SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
• Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs
• Recursive Graphical Neural Networks for Text Classification
• A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
• Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection
• DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
• Socially-Aware Self-Supervised Tri-Training for Recommendation
• RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution
• Link prediction in dynamic networks using random dot product graphs
• Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
• Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
• PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
• Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
• Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids
• RepGN:Object Detection with Relational Proposal Graph Network
• Can Graph Neural Networks Help Logic Reasoning?
• GraphTSNE: A Visualization Technique for Graph-Structured Data
• Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
• Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery
• Principled Simplicial Neural Networks for Trajectory Prediction
• On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
• Knowledge-aware Contrastive Molecular Graph Learning
• Directional Graph Networks
• Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
• On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
• Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
• VulSPG: Vulnerability detection based on slice property graph representation learning
• Heterogeneous Information Network-based Interest Composition with Graph Neural Network for Recommendation
• Iterative Graph Self-Distillation
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
• Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
• Learning Edge Properties in Graphs from Path Aggregations
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
• Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
• Learning Graph-Level Representations with Recurrent Neural Networks
• PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
• Data Considerations in Graph Representation Learning for Supply Chain Networks
• Context-Aware Visual Compatibility Prediction
• Integrating LSTMs and GNNs for COVID-19 Forecasting
• Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
• Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach
• ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
• Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
• Network Representation Learning: Consolidation and Renewed Bearing
• Rubik: A Hierarchical Architecture for Efficient Graph Learning
• Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
• Relational Graph Learning for Crowd Navigation
• EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
• On Graph Neural Networks versus Graph-Augmented MLPs
• Pose-based Modular Network for Human-Object Interaction Detection
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
• Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
• Bioinformatics and Medicine in the Era of Deep Learning
• Graph Convolutional Networks with EigenPooling
• DeepTrax: Embedding Graphs of Financial Transactions
• On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
• You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
• Room Classification on Floor Plan Graphs using Graph Neural Networks
• SCE: Scalable Network Embedding from Sparsest Cut
• VersaGNN: a Versatile accelerator for Graph neural networks
• RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation
• Lifelong Graph Learning
• Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
• Size-Invariant Graph Representations for Graph Classification Extrapolations
• Siamese Graph Neural Networks for Data Integration
• Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
• Constant Time Graph Neural Networks
• L${}^2$-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
• Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning
• Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
• Information Obfuscation of Graph Neural Networks
• Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning
• AffinityNet: semi-supervised few-shot learning for disease type prediction
• Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning
• Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
• Graph Neural Pre-training for Enhancing Recommendations using Side Information
• The Impact of Global Structural Information in Graph Neural Networks Applications
• Learning and Interpreting Multi-Multi-Instance Learning Networks
• End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection
• Artificial Intelligence in Drug Discovery: Applications and Techniques
• Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
• Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification
• Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
• Secure Deep Graph Generation with Link Differential Privacy
• Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
• Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
• Simplicial Complex Representation Learning
• Learning Color Compatibility in Fashion Outfits
• Outlier Aware Network Embedding for Attributed Networks
• Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
• Jointly embedding the local and global relations of heterogeneous graph for rumor detection
• Graph-Revised Convolutional Network
• GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification
• Node Masking: Making Graph Neural Networks Generalize and Scale Better
• Topology-Aware Graph Pooling Networks
• A Lagrangian Approach to Information Propagation in Graph Neural Networks
• Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
• Multi-Channel Graph Convolutional Networks
• SPINE: Structural Identity Preserved Inductive Network Embedding
• Graph Node-Feature Convolution for Representation Learning
• Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs
• Learning Representations of Missing Data for Predicting Patient Outcomes
• Attributed Network Embedding for Incomplete Attributed Networks
• Stochastic Aggregation in Graph Neural Networks
• Self-supervised edge features for improved Graph Neural Network training
• MutualGraphNet: A novel model for motor imagery classification
• Sequential Graph Convolutional Network for Active Learning
• Graph2Seq: Scalable Learning Dynamics for Graphs
• Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
• DAGCN: Dual Attention Graph Convolutional Networks
• Low-Rank Subspaces for Unsupervised Entity Linking
• Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
• SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
• SDGNN: Learning Node Representation for Signed Directed Networks
• Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
• Hierarchical Message-Passing Graph Neural Networks
• Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
• Recurrent Graph Syntax Encoder for Neural Machine Translation
• Learning Scalable Structural Representations for Link Prediction with Bloom Signatures
• Simplifying Architecture Search for Graph Neural Network
• Deoscillated Graph Collaborative Filtering
• Heterogeneous Graph Matching Networks
• TGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking
• Modeling Attention Flow on Graphs
• Non-Parametric Graph Learning for Bayesian Graph Neural Networks
• EPNE: Evolutionary Pattern Preserving Network Embedding
• Local Density Estimation in High Dimensions
• Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
• Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview
• Graph-Aware Transformer: Is Attention All Graphs Need?
• Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
• Bipartite Graph Embedding via Mutual Information Maximization
• SQL-to-Text Generation with Graph-to-Sequence Model
• Few-shot Network Anomaly Detection via Cross-network Meta-learning
• Hierarchically Regularized Deep Forecasting
• Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks
• A comparative study of similarity-based and GNN-based link prediction approaches
• R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
• DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
• Fusion Graph Convolutional Networks
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
• Feature Propagation on Graph: A New Perspective to Graph Representation Learning
• LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
• Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art
• Exact Recovery of Community Structures Using DeepWalk and Node2vec
• Deep Q-Learning for Directed Acyclic Graph Generation
• GraphAttacker: A General Multi-Task GraphAttack Framework
• Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
• Set-to-Sequence Methods in Machine Learning: a Review
• ANAE: Learning Node Context Representation for Attributed Network Embedding
• CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering
• Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
• Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing
• Few-Shot Knowledge Graph Completion
• Graph Neural Network Based VC Investment Success Prediction
• Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
• Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks
• Positional Encoder Graph Neural Networks for Geographic Data
• LT-OCF: Learnable-Time ODE-based Collaborative Filtering
• Convolutional Geometric Matrix Completion
• Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
• Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
• ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
• Topology and Content Co-Alignment Graph Convolutional Learning
• Density-Aware Graph for Deep Semi-Supervised Visual Recognition
• Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
• SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
• Jointly Attacking Graph Neural Network and its Explanations
• Exploring the Representational Power of Graph Autoencoder
• Generating Logical Forms from Graph Representations of Text and Entities
• GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
• Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
• Attacking Black-box Recommendations via Copying Cross-domain User Profiles
• Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
• QUINT: Node embedding using network hashing
• Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
• PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery
• Graph Few-shot Learning via Knowledge Transfer
• Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
• Message Passing Query Embedding
• HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs
• A framework for constructing a huge name disambiguation dataset: algorithms, visualization and human collaboration
• Learning to Cluster Faces via Confidence and Connectivity Estimation
• Graph Homomorphism Convolution
• Attentional Multilabel Learning over Graphs: A Message Passing Approach
• Graph Embedding for Recommendation against Attribute Inference Attacks
• GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
• Graph Neighborhood Attentive Pooling
• A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users
• Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
• A Hyperbolic-to-Hyperbolic Graph Convolutional Network
• Distributed Training of Graph Convolutional Networks using Subgraph Approximation
• Markov-Lipschitz Deep Learning
• A pipeline for fair comparison of graph neural networks in node classification tasks
• Unsupervised Deep Manifold Attributed Graph Embedding
• An Overview on the Application of Graph Neural Networks in Wireless Networks
• Policy-GNN: Aggregation Optimization for Graph Neural Networks
• Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
• Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition
• Edge Proposal Sets for Link Prediction
• High-resolution rainfall-runoff modeling using graph neural network
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
• VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
• Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
• Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
• Adversarial Graph Disentanglement
• GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
• LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs
• Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
• Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
• CogDL: A Comprehensive Library for Graph Deep Learning
• Primitive Representation Learning for Scene Text Recognition
• Independence Promoted Graph Disentangled Networks
• Graph Factorization Machines for Cross-Domain Recommendation
• Which way? Direction-Aware Attributed Graph Embedding
• Improving Location Recommendation with Urban Knowledge Graph
• Solving Machine Learning Problems
• Quaternion Graph Neural Networks
• Metapath- and Entity-aware Graph Neural Network for Recommendation
• MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
• Spectral Temporal Graph Neural Network for Trajectory Prediction
• Tree Decomposed Graph Neural Network
• Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
• Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
• EchoEA: Echo Information between Entities and Relations for Entity Alignment
• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
• GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks
• Node Classification Meets Link Prediction on Knowledge Graphs
• Discovering Supply Chain Links with Augmented Intelligence
• Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks
• SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network
• Sampling and Recovery of Graph Signals based on Graph Neural Networks
• Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
• Heterogeneous Graph Collaborative Filtering
• CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
• GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
• $p$-Laplacian Based Graph Neural Networks
• GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
• Hop-Hop Relation-aware Graph Neural Networks
• Reconstruction for Powerful Graph Representations
• LookHops: light multi-order convolution and pooling for graph classification
• DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
• Molecular distance matrix prediction based on graph convolutional networks
• JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms
• Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
• Generalization bounds for graph convolutional neural networks via Rademacher complexity
• Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
• Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network
• Graph Cross Networks with Vertex Infomax Pooling
• Accelerating science with human versus alien artificial intelligences
• Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
• Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
• Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
• Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
• Next Waves in Veridical Network Embedding
• Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
• Enhancing the Association in Multi-Object Tracking via Neighbor Graph
• A benchmark study on reliable molecular supervised learning via Bayesian learning
• A Novel Higher-order Weisfeiler-Lehman Graph Convolution
• ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks
• Collaborative Motion Prediction via Neural Motion Message Passing
• Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description
• Dynamic Graph Representation for Partially Occluded Biometrics
• Quick survey of graph-based fraud detection methods
• Parallel Computation of Graph Embeddings
• Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
• Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes
• MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
• Feature Interaction-aware Graph Neural Networks
• Initialization for Network Embedding: A Graph Partition Approach
• Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
• Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
• Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
• AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification
• Equivariant Entity-Relationship Networks
• Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
• SINE: Scalable Incomplete Network Embedding
• Attributed Network Embedding via Subspace Discovery
• Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
• Hierarchical Protein Function Prediction with Tail-GNNs
• Multimodal and Contrastive Learning for Click Fraud Detection
• Multi-Graph Convolution Collaborative Filtering
• Towards Plausible Graph Anonymization
• HyGCN: A GCN Accelerator with Hybrid Architecture
• TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
• What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data
• Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding
• Learning Hierarchical Review Graph Representations for Recommendation
• Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better
• On Self-Distilling Graph Neural Network
• Heterogeneous Hypergraph Embedding for Graph Classification
• BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
• Evolution of a Web-Scale Near Duplicate Image Detection System
• Embeddings and Representation Learning for Structured Data
• Exploring Global Information for Session-based Recommendation
• Releasing Graph Neural Networks with Differential Privacy Guarantees
• Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
• Building Segmentation through a Gated Graph Convolutional Neural Network with Deep Structured Feature Embedding
• HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
• Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning
• Predicting Mergers and Acquisitions using Graph-based Deep Learning
• Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
• Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
• An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
• Graph Classification by Mixture of Diverse Experts
• Graph Highway Networks
• Lossless Compression of Structured Convolutional Models via Lifting
• A Robust and Generalized Framework for Adversarial Graph Embedding
• Explainable Link Prediction for Privacy-Preserving Contact Tracing
• A Biased Graph Neural Network Sampler with Near-Optimal Regret
• PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
• Multi-Level Graph Contrastive Learning
• Continuous Graph Flow
• NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection
• GRADE: Graph Dynamic Embedding
• Language in Our Time: An Empirical Analysis of Hashtags
• Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
• GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
• Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
• Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
• GraphMI: Extracting Private Graph Data from Graph Neural Networks
• ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
• Knowledge Enhanced Multi-modal Fake News Detection
• Dual-Attention Graph Convolutional Network
• Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
• Recurrent Graph Neural Networks for Rumor Detection in Online Forums
• Hierarchical Graph Networks for 3D Human Pose Estimation
• GraLSP: Graph Neural Networks with Local Structural Patterns
• Better Schedules for Low Precision Training of Deep Neural Networks
• Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models
• Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
• Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
• Graph Information Bottleneck for Subgraph Recognition
• Inductive Learning on Commonsense Knowledge Graph Completion
• Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
• Embedding Graphs on Grassmann Manifold
• Attention improves concentration when learning node embeddings
• Drug Package Recommendation via Interaction-aware Graph Induction
• A Graph Convolutional Network Composition Framework for Semi-supervised Classification
• Hypergraph Pre-training with Graph Neural Networks
• Signed Graph Attention Networks
• GraphITE: Estimating Individual Effects of Graph-structured Treatments
• Generating a Doppelganger Graph: Resembling but Distinct
• Tackling Graphical NLP problems with Graph Recurrent Networks
• Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
• Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders
• Learning the Implicit Semantic Representation on Graph-Structured Data
• Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features
• Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism
• An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
• Dynamic Sequential Graph Learning for Click-Through Rate Prediction
• GmCN: Graph Mask Convolutional Network
• SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction
• Answering Any-hop Open-domain Questions with Iterative Document Reranking
• AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting
• Graph Belief Propagation Networks
• Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection
• CoulGAT: An Experiment on Interpretability of Graph Attention Networks
• Better Feature Integration for Named Entity Recognition
• Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
• Message Passing in Graph Convolution Networks via Adaptive Filter Banks
• Data-Efficient Graph Embedding Learning for PCB Component Detection
• Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
• Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
• Gaussian-Induced Convolution for Graphs
• Towards Graph Representation Learning in Emergent Communication
• GCN for HIN via Implicit Utilization of Attention and Meta-paths
• SoGCN: Second-Order Graph Convolutional Networks
• LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
• ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data
• HR-RCNN: Hierarchical Relational Reasoning for Object Detection
• ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
• AppQ: Warm-starting App Recommendation Based on View Graphs
• Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks
• Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
• Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
• Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation
• Learning Hierarchical Graph Neural Networks for Image Clustering
• Local2Global: Scaling global representation learning on graphs via local training
• Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
• Robust Hierarchical Graph Classification with Subgraph Attention
• Watermarking Graph Neural Networks based on Backdoor Attacks
• Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
• BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
• User Preference-aware Fake News Detection
• Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters
• Tackling the Local Bias in Federated Graph Learning
• Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
• Efficient and Interpretable Robot Manipulation with Graph Neural Networks
• Neuralizing Efficient Higher-order Belief Propagation
• Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
• Deep Generative Modeling in Network Science with Applications to Public Policy Research
• Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
• Adversarial Deep Network Embedding for Cross-network Node Classification
• Generalized Embedding Machines for Recommender Systems
• On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
• Structural Inductive Biases in Emergent Communication
• Graph Neural Networks with Feature and Structure Aware Random Walk
• GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
• Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
• I-GCN: Robust Graph Convolutional Network via Influence Mechanism
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
• Structure-Aware Label Smoothing for Graph Neural Networks
• Explainable Recommender Systems via Resolving Learning Representations
• Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
• Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
• Cyclic Label Propagation for Graph Semi-supervised Learning
• Graph Representation Learning via Hard and Channel-Wise Attention Networks
• From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
• Stacked Graph Filter
• Recurrent Attention Walk for Semi-supervised Classification
• Polyp-artifact relationship analysis using graph inductive learned representations
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification
• MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
• CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
• Learning Feature Aggregation for Deep 3D Morphable Models
• A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data
• Graph Random Neural Features for Distance-Preserving Graph Representations
• Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
• Hybrid Low-order and Higher-order Graph Convolutional Networks
• Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
• Efficient Colon Cancer Grading with Graph Neural Networks
• Multi-Modal Retrieval using Graph Neural Networks
• Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network
• Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
• Fake News Detection through Graph Comment Advanced Learning
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
• AiAds: Automated and Intelligent Advertising System for Sponsored Search
• LEReg: Empower Graph Neural Networks with Local Energy Regularization
• Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
• GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
• Social Fraud Detection Review: Methods, Challenges and Analysis
• A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
• On Local Aggregation in Heterophilic Graphs
• CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
• Understanding and Tackling Over-Dilution in Graph Neural Networks
• Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
• EGC2: Enhanced Graph Classification with Easy Graph Compression
• Graph Neural Networks for Node-Level Predictions
• Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View
• A Universal Model for Cross Modality Mapping by Relational Reasoning
• Deep Constraint-based Propagation in Graph Neural Networks
• Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
• MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks
• Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
• Inductive Subgraph Embedding for Link Prediction
• Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network
• Temporal Meta-path Guided Explainable Recommendation
• Balanced Order Batching with Task-Oriented Graph Clustering
• Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
• TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning
• Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
• Personalized Hashtag Recommendation for Micro-videos
• Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
• Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
• Jointly Learnable Data Augmentations for Self-Supervised GNNs
• A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
• Neural Subgraph Isomorphism Counting
• CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration
• Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
• Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
• AHINE: Adaptive Heterogeneous Information Network Embedding
• Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
• Graphs, Entities, and Step Mixture
• Topological Effects on Attacks Against Vertex Classification
• GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
• SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
• Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
• Spectral Network Embedding: A Fast and Scalable Method via Sparsity
• Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
• Inductive learning for product assortment graph completion
• Deep Representation Learning For Multimodal Brain Networks
• Learning Actor Relation Graphs for Group Activity Recognition
• Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective
• Artist Similarity with Graph Neural Networks
• HEAT: Hyperbolic Embedding of Attributed Networks
• Learning to Cluster Faces on an Affinity Graph
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations
• Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search
• Graph Filtration Learning
• HOPF: Higher Order Propagation Framework for Deep Collective Classification
• Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
• Computing Steiner Trees using Graph Neural Networks
• Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
• Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
• Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
• Single-Layer Graph Convolutional Networks For Recommendation
• Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
• TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
• Learning Robust Representations with Graph Denoising Policy Network
• ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks
• Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
• Advances in Collaborative Filtering and Ranking
• Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
• Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
• Structural Optimization Makes Graph Classification Simpler and Better
• Node Feature Kernels Increase Graph Convolutional Network Robustness
• DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback
• BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
• Lorentzian Graph Convolutional Networks
• Learning Conjoint Attentions for Graph Neural Nets
• Search Efficient Binary Network Embedding
• LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator
• Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning
• GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
• Measuring Research Interest Similarity with Transition Probabilities
• Latent Network Embedding via Adversarial Auto-encoders
• Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction
• weg2vec: Event embedding for temporal networks
• Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks
• Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
• mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding
• Connecting Graph Convolutional Networks and Graph-Regularized PCA
• Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
• Problem Learning: Towards the Free Will of Machines
• Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
• REMOD: Relation Extraction for Modeling Online Discourse
• Smart Vectorizations for Single and Multiparameter Persistence
• Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction
• Sparse-Interest Network for Sequential Recommendation
• Transferable Graph Optimizers for ML Compilers
• Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
• COLOGNE: Coordinated Local Graph Neighborhood Sampling
• RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
• Improving Expressivity of Graph Neural Networks
• Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition
• Lookup subnet based Spatial Graph Convolutional neural Network
• Convolutions for Spatial Interaction Modeling
• Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
• Fully Hyperbolic Graph Convolution Network for Recommendation
• SAS: A Simple, Accurate and Scalable Node Classification Algorithm
• Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
• Relevant Region Prediction for Crowd Counting
• A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking
• Fast Graph Learning with Unique Optimal Solutions
• Pre-Trained Models for Heterogeneous Information Networks
• Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
• SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features
• Graph Representation Learning Network via Adaptive Sampling
• OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation
• Cascade Image Matting with Deformable Graph Refinement
• Non-Recursive Graph Convolutional Networks
• Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
• Graph Feature Gating Networks
• DFraud3- Multi-Component Fraud Detection freeof Cold-start
• Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
• A Graph Neural Network Approach for Product Relationship Prediction
• Graph Pooling with Node Proximity for Hierarchical Representation Learning
• NEMR: Network Embedding on Metric of Relation
• Learn Dynamic-Aware State Embedding for Transfer Learning
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
• Deep Fraud Detection on Non-attributed Graph
• SPAN: Subgraph Prediction Attention Network for Dynamic Graphs
• Action Recognition with Kernel-based Graph Convolutional Networks
• Heterogeneous Graph Neural Network with Multi-view Representation Learning
• Graph Structural-topic Neural Network
• Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
• Node Copying for Protection Against Graph Neural Network Topology Attacks
• Deep Grouping Model for Unified Perceptual Parsing
• LoCEC: Local Community-based Edge Classification in Large Online Social Networks
• Affinity Graph Supervision for Visual Recognition
• Learn to Propagate Reliably on Noisy Affinity Graphs
• A Collective Learning Framework to Boost GNN Expressiveness
• New Insights into Graph Convolutional Networks using Neural Tangent Kernels
• Supervised learning on heterogeneous, attributed entities interacting over time
• An Uncoupled Training Architecture for Large Graph Learning
• Node Attribute Generation on Graphs
• Saliency Prediction with External Knowledge
• Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure
• An Experimental Study of the Transferability of Spectral Graph Networks
• Graph Neural Network Training with Data Tiering
• LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs
• Spectral Transform Forms Scalable Transformer
• Fractional order graph neural network
• A Block-based Generative Model for Attributed Networks Embedding
• Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection
• Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
• DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
• Decoupled Variational Embedding for Signed Directed Networks
• Mutual Teaching for Graph Convolutional Networks
• Beyond Observed Connections : Link Injection
• GRAPHSPY: Fused Program Semantic-Level Embedding via Graph Neural Networks for Dead Store Detection
• Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing
• Layer-stacked Attention for Heterogeneous Network Embedding
• SCG: Spotting Coordinated Groups in Social Media
• Fact Checking via Path Embedding and Aggregation
• Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
• Towards Self-Explainable Graph Neural Network
• Data-Driven Self-Supervised Graph Representation Learning
• Toward Edge-Centric Network Embeddings
• Alleviating Over-Smoothing via Aggregation over Compact Manifolds
• BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization
• Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
• Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
• Dynamic Joint Variational Graph Autoencoders
• Embedding Dynamic Attributed Networks by Modeling the Evolution Processes
• Fast Haar Transforms for Graph Neural Networks
• DeepDrawing: A Deep Learning Approach to Graph Drawing
• Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
• Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
• Learning Correlated Latent Representations with Adaptive Priors
• Edge-featured Graph Neural Architecture Search
• GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
• Cascading: Association Augmented Sequential Recommendation
• Network Classifiers With Output Smoothing
• Sublinear Update Time Randomized Algorithms for Dynamic Graph Regression
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
• Experiments with graph convolutional networks for solving the vertex $p$-center problem
• Context-aware Tree-based Deep Model for Recommender Systems
• Can NetGAN be improved on short random walks?
• Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains
• The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite Hypergraphs
• DNA-GCN: Graph convolutional networks for predicting DNA-protein binding
• Subset Node Representation Learning over Large Dynamic Graphs
• Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
• Interferometric Graph Transform for Community Labeling
• CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph
• FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
• Transportation Scenario Planning with Graph Neural Networks
• Dynamic Embedding on Textual Networks via a Gaussian Process
• When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
• Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations
• Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
• Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
• Scaling Up Graph Neural Networks Via Graph Coarsening
• Timestamping Documents and Beliefs
• Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
• Characterizing and Understanding GCNs on GPU
• Optimizing Graph Transformer Networks with Graph-based Techniques
• Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
• Step Out of Your Comfort Zone: More Inclusive Content Recommendation for Networked Systems
• Multi-Stage Network Embedding for Exploring Heterogeneous Edges
• Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
• MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning
• Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
• OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
• DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
• Visualising Argumentation Graphs with Graph Embeddings and t-SNE
• Multi-modal Graph Learning for Disease Prediction
• Enhanced Network Embeddings via Exploiting Edge Labels
• Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
• FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation
• Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
• Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks
• Automated Graph Learning via Population Based Self-Tuning GCN
• STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction
• Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
• Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
• Effective Eigendecomposition based Graph Adaptation for Heterophilic Networks
• U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
• CoRGi: Content-Rich Graph Neural Networks with Attention
• Signed Bipartite Graph Neural Networks
• Bidirectional group random walk based network embedding for asymmetric proximity
• Structure-Aware Face Clustering on a Large-Scale Graph with $10^7$ Nodes
• Permutation-Invariant Subgraph Discovery
• Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
• Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension
• Memory-Associated Differential Learning
• Diversified Multiscale Graph Learning with Graph Self-Correction
• Co-embedding of Nodes and Edges with Graph Neural Networks
• On the Global Self-attention Mechanism for Graph Convolutional Networks
• Modeling Heterogeneous Edges to Represent Networks with Graph Auto-Encoder
• Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems
• Network Representation Learning: From Traditional Feature Learning to Deep Learning
• GitEvolve: Predicting the Evolution of GitHub Repositories