Fast Graph Representation Learning with PyTorch Geometric

2019/03/06 by Matthias Fey, Jan Eric Lenssen, Fey, Matthias +1 · 1,260 citations
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #CUDA #Computational science #Computer science #Data Visualization and Analytics #Deep learning #Graph #Graph Theory and Algorithms #Parallel computing #Point cloud #Theoretical computer science #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1903.02428

published in arXiv (Cornell University) (Cornell University) · ICLR 2019 (RLGM Workshop)

openalex publication_date 2019/03/06 · openalex created_date 2019/03/11 · arxiv created 2019/04/25 · arxiv updated 2019/04/26 · openalex updated_date 2026/07/28

Abstract

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios.

Citations

• Variational Graph Auto-Encoders
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Cited by

• What Matters in Deep Learning for Time Series Forecasting?
• MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning
• ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
• Learned, Relied Upon, or Necessary? Separating Checkpoint Dependence from Task-Level Value in Sheaf GNNs
• Graph Neural Networks for Predicting Solvability of Finite Groups
• Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
• Plain Transformers are Surprisingly Powerful Link Predictors
• SparScene: Efficient Traffic Scene Representation via Sparse Graph Learning for Large-Scale Trajectory Generation
• SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication
• Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes
• Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
• Composition-agnostic prediction of self-assembly in multicomponent amphiphile mixtures from molecular structure
• gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
• ProtoFlow: Interpretable and Robust Surgical Workflow Modeling with Learned Dynamic Scene Graph Prototypes
• Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
• View Space: Learning Representation across Arbitrary Graphs
• Node Feature Kernels Increase Graph Convolutional Network Robustness
• DAPO: Design Structure-Aware Pass Ordering in High-Level Synthesis with Graph Contrastive and Reinforcement Learning
• Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models
• gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs
• Empowering GNNs for Domain Adaptation via Denoising Target Graph
• Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
• NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation
• GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search
• Morphling: Fast, Fused, and Flexible GNN Training at Scale
• QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions
• ARES: Anomaly Recognition Model For Edge Streams
• EZ-SP: Fast and Lightweight Superpoint-Based 3D Segmentation
• Provably Powerful Graph Networks
• Nodal Hybrid Neural Solvers for Parametric PDE Systems
• Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction
• Accumulated Local Effects and Graph Neural Networks for link prediction
• Rethinking Message Passing Neural Networks with Diffusion Distance-guided Stress Majorization
• On the Optimality of Discrete Object Naming: a Kinship Case Study
• Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodings
• Comparative Analysis of Precipitation Forecasts between the ECMWF Artificial Intelligence System (AIFS) and Its Integrated Forecast System (IFS)
• LMSeg: an end-to-end geometric message-passing network on barycentric dualgraphs for large-scale landscape mesh segmentation
• Pre-training Graph Neural Networks on 2D and 3D Molecular Structures by using Multi-View Conditional Information Bottleneck
• GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
• Protein Set Transformer: a protein-based genome language model to power high-diversity viromics
• Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models
• PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning
• Optimizing Quantum Key Distribution Network Performance using Graph Neural Networks
• HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
• N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
• Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention
• AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
• Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
• PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
• AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention
• FuseSampleAgg: Fused Neighbor Sampling and Aggregation for Mini-batch GNNs
• Edge-aware baselines for ogbn-proteins in PyTorch Geometric: species-wise normalization, post-hoc calibration, and cost-accuracy trade-offs
• Learning to Solve Resource-Constrained Project Scheduling Problems with Duration Uncertainty using Graph Neural Networks
• InteractiveGNNExplainer: A Visual Analytics Framework for Multi-Faceted Understanding and Probing of Graph Neural Network Predictions
• Understanding graph embedding methods and their applications
• Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
• Search to aggregate neighborhood for graph neural network
• Improving the Generalisation of Learned Reconstruction Frameworks
• Decimated Framelet System on Graphs and Fast G-Framelet Transforms
• Drug-Target Interaction Prediction with Graph Attention networks
• Graph Condensation for Graph Neural Networks
• Edge Representation Learning with Hypergraphs
• ReCoG: A Deep Learning Framework with Heterogeneous Graph for Interaction-Aware Trajectory Prediction
• Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping
• Simplifying Architecture Search for Graph Neural Network
• Mesh-based Super-resolution of Detonation Flows with Multiscale Graph Transformers
• MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging
• On topological descriptors for graph products
• Direct Molecular Polarizability Prediction with SO(3) Equivariant Local Frame GNNs
• Heterogeneous Graph Transformer
• Learning Time-Varying Graph Signals via Koopman
• Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity
• Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks
• FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow
• Hardware-Accelerated GNN-based Hit Filtering for the Belle II Level-1 Trigger
• wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
• GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization
• A Transferable Machine Learning Approach to Predict Quantum Circuit Parameters for Electronic Structure Problems
• BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction
• Graph-Based Alternatives to LLMs for Human Simulation
• X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction
• Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
• MeixnerNet: Adaptive and Robust Spectral Graph Neural Networks with Discrete Orthogonal Polynomials
• Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
• Towards a predictive spatio-temporal representation of brain data
• NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
• Accelerating 3D Deep Learning with PyTorch3D
• Rethinking pooling in graph neural networks
• ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
• Strategies for Pre-training Graph Neural Networks
• Multipole Graph Neural Operator for Parametric Partial Differential Equations
• Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
• Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks
• Lifelong Graph Learning
• Open Graph Benchmark: Datasets for Machine Learning on Graphs
• Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
• Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
• Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent
• Generalization of Graph Neural Network Models for Distribution Grid Fault Detection
• Inductive Matrix Completion Based on Graph Neural Networks
• Policy-GNN: Aggregation Optimization for Graph Neural Networks
• Graph neural networks: A review of methods and applications
• Causal Effect Inference for Structured Treatments
• GPT-GNN: Generative Pre-Training of Graph Neural Networks
• Graph Geometry Interaction Learning
• On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering
• Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
• A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
• TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
• Are Graph Neural Networks Miscalibrated?
• Graph Coarsening with Neural Networks
• Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
• Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model
• Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
• Graph Neural Networks: Methods, Applications, and Opportunities
• Curvature Graph Neural Network
• MAGNET: A Multi-Graph Attentional Network for Code Clone Detection
• TriDS: AI-native molecular docking framework unified with binding site identification, conformational sampling and scoring
• On Self-Distilling Graph Neural Network
• Understanding Isomorphism Bias in Graph Data Sets
• If You Want to Be Robust, Be Wary of Initialization
• Enhancing Graph Classification Robustness with Singular Pooling
• Self-Attention Graph Pooling
• Adaptive Universal Generalized PageRank Graph Neural Network
• A Hybrid GNN-LSE Method for Fast, Robust, and Physically-Consistent AC Power Flow
• Relieving the Over-Aggregating Effect in Graph Transformers
• Deep Graph Contrastive Representation Learning
• Learning to Triage Taint Flows Reported by Dynamic Program Analysis in Node.js Packages
• Drug-disease networks and drug repurposing
• AegisMCP: Online Graph Intrusion Detection for Tool-Augmented LLMs on Edge Devices
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
• Robustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing
• Simple and Efficient Heterogeneous Temporal Graph Neural Network
• Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
• Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
• PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
• Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
• On The Expressive Power of GNN Derivatives
• RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction
• Composition-based Multi-Relational Graph Convolutional Networks
• Graph Neural Networks: A Review of Methods and Applications
• Message Passing on the Edge: Towards Scalable and Expressive GNNs
• Towards Affordable, Adaptive and Automatic GNN Training on CPU-GPU Heterogeneous Platforms
• AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining
• Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models
• ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments
• Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
• Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
• Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
• Preference-driven Knowledge Distillation for Few-shot Node Classification
• Open Biomedical Network Benchmark A Python Toolkit for Benchmarking Datasets with Biomedical Networks
• Understanding and Resolving Performance Degradation in Graph Convolutional Networks
• Graph Neural Network Training with Data Tiering
• Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
• SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
• Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty
• Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
• SGAS: Sequential Greedy Architecture Search
• TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
• Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
• Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
• GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
• Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
• Test-Time Search in Neural Graph Coarsening Procedures for the Capacitated Vehicle Routing Problem
• STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation
• VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
• Neural Operator: Graph Kernel Network for Partial Differential Equations
• DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
• Equivariance by Local Canonicalization: A Matter of Representation
• Are neural scaling laws leading quantum chemistry astray?
• Identifying obfuscated code through graph-based semantic analysis of binary code
• SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
• SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
• Neural Message-Passing on Attention Graphs for Hallucination Detection
• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
• Monotonic Transformation Invariant Multi-task Learning
• Weighing the Milky Way and Andromeda with Artificial Intelligence
• Probabilistic Numeric Convolutional Neural Networks
• CrediBench: Building Web-Scale Network Datasets for Information Integrity
• HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
• HELIOS: Hierarchical Exploration for Language-grounded Interaction in Open Scenes
• Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
• Rebuild AC Power Flow Models with Graph Attention Networks
• OmniPlantSeg: Species Agnostic 3D Point Cloud Organ Segmentation for High-Resolution Plant Phenotyping Across Modalities
• Semi-supervised Anomaly Detection on Attributed Graphs
• C²MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis
• SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
• Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
• Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
• Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
• AGL: a Scalable System for Industrial-purpose Graph Machine Learning
• GRIPHIN: grids of pharmacophore interaction fields for affinity prediction
• Message Passing Networks for Molecules with Tetrahedral Chirality
• Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
• LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow
• ViG-LRGC: Vision Graph Neural Networks with Learnable Reparameterized Graph Construction
• SAS: A Simple, Accurate and Scalable Node Classification Algorithm
• Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
• DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
• Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
• Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents
• Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning
• Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
• RelEx: A Model-Agnostic Relational Model Explainer
• Metapath- and Entity-aware Graph Neural Network for Recommendation
• From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
• Graph Neural Networks Including Sparse Interpretability
• Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning
• Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
• Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks
• Optimal message passing for molecular prediction is simple, attentive and spatial
• Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
• NuGraph2 with Explainability: Post-hoc Explanations for Geometric Neural Network Predictions
• Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
• A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
• Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
• How Attentive are Graph Attention Networks?
• BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
• Track Seeding and Labelling with Embedded-space Graph Neural Networks
• Towards Robust Graph Contrastive Learning
• Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation
• Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
• A guide to machine learning for biologists
• Discovering Symbolic Models from Deep Learning with Inductive Biases
• Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
• TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph
• Rashomon in the Streets: Explanation Ambiguity in Scene Understanding
• Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
• A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts
• Quantum-Enhanced Optimization by Warm Starts
• HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
• Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size
• What Expressivity Theory Misses: Message Passing Complexity for GNNs
• A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
• Independent Prototype Propagation for Zero-Shot Compositionality
• Efficient Colon Cancer Grading with Graph Neural Networks
• GLaRE: A Graph-based Landmark Region Embedding Network for Emotion Recognition
• Controllable Open-ended Question Generation with A New Question Type Ontology
• Weisfeiler and Lehman Go Cellular: CW Networks
• Multimodal Conditional MeshGAN for Personalized Aneurysm Growth Prediction
• Memorization in Graph Neural Networks
• DeeperGCN: All You Need to Train Deeper GCNs
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
• Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
• Fast Graph Neural Network for Image Classification
• Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
• Nested Graph Neural Networks
• HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation
• Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams
• Explaining Data Anomalies over the NMSSM Parameter Space with Deep Learning Techniques
• Personalized Subgraph Federated Learning with Sheaf Collaboration
• Ranking over Regression for Bayesian Optimization and Molecule Selection
• GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
• One-Class Intrusion Detection with Dynamic Graphs
• Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
• Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems
• Task-Agnostic Morphology Evolution
• DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning
• Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
• Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules
• Re-thinking Memory-Bound Limitations in CGRAs
• Contextual Scene Augmentation and Synthesis via GSACNet
• Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring
• Exact Verification of Graph Neural Networks with Incremental Constraint Solving
• Meta-learning optimizes predictions of missing links in real-world networks
• Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs
• TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
• On Understanding of the Dynamics of Model Capacity in Continual Learning
• Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph
• Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
• Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection
• Discriminative structural graph classification
• Learning Geometric-Aware Quadrature Rules for Functional Minimization
• Will You Be Aware? Eye Tracking-Based Modeling of Situational Awareness in Augmented Reality
• Functional Connectivity Graph Neural Networks
• Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape
• Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
• Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
• Learned Low Precision Graph Neural Networks
• Rubik: A Hierarchical Architecture for Efficient Graph Learning
• Adaptive Riemannian Graph Neural Networks
• GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction
• Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
• Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling
• PyG 2.0: Scalable Learning on Real World Graphs
• Boosting Sensitivity to HH→ bb γγ with Graph Neural Networks and XGBoost
• Explaining GNN Explanations with Edge Gradients
• Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts
• Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
• GraphiT: Encoding Graph Structure in Transformers
• FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming
• HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
• Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
• Graph neural networks for residential location choice: connection to classical logit models
• MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
• Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
• A pipeline for fair comparison of graph neural networks in node classification tasks
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
• Universal crystal material property prediction via multi-view geometric fusion in graph transformers
• SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
• Lossless Compression of Structured Convolutional Models via Lifting
• Graph Information Bottleneck
• Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
• Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
• Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
• ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
• Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
• Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors
• BiPointNet: Binary Neural Network for Point Clouds
• A Dynamic Reduction Network for Point Clouds
• Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
• Event-based Graph Representation with Spatial and Motion Vectors for Asynchronous Object Detection
• Graph Attention Networks for Detecting Epilepsy from EEG Signals Using Accessible Hardware in Low-Resource Settings
• Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion
• A Gentle Introduction to Deep Learning for Graphs
• CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
• Scaling Up Graph Neural Networks Via Graph Coarsening
• Adaptive Visibility Graph Neural Network and its Application in Modulation Classification
• Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
• Research on the application of graph data structure and graph neural network in node classification/clustering tasks
• Belief Propagation Neural Networks
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
• MAAS: Multi-modal Assignation for Active Speaker Detection
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
• LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges
• Breaking the Expressive Bottlenecks of Graph Neural Networks
• BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
• Wasserstein Embedding for Graph Learning
• Attention-Based Learning on Molecular Ensembles
• Curriculum learning for multilevel budgeted combinatorial problems
• Deep learning approaches to top FCNC couplings to photons at the LHC
• Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
• SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
• Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
• BikeVAE-GNN: A Variational Autoencoder-Augmented Hybrid Graph Neural Network for Sparse Bicycle Volume Estimation
• Benchmarking Graph Neural Networks on Link Prediction
• How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
• Graph-Revised Convolutional Network
• DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
• Calibrating and Improving Graph Contrastive Learning
• TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
• Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
• Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
• From Local Structures to Size Generalization in Graph Neural Networks
• Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
• Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
• Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
• PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
• HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
• TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
• BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
• SurfR: Surface Reconstruction with Multi-scale Attention
• A Pre-training Framework for Relational Data with Information-theoretic Principles
• AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
• Geometric Generative Modeling with Noise-Conditioned Graph Networks
• Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
• SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
• Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
• Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
• Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches
• GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks
• Learnable quantum spectral filters for hybrid graph neural networks
• Bit-Flip Fault Attack: Crushing Graph Neural Networks via Gradual Bit Search
• Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses
• Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
• Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Clouds
• Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
• Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
• Effective Capacitance Modeling Using Graph Neural Networks
• Graph Neural Networks for Electricity Load Forecasting
• A Universal Approach to Feature Representation in Dynamic Task Assignment Problems
• Explainable Link Prediction for Privacy-Preserving Contact Tracing
• RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
• Enhancing Swarms Durability to Threats via Graph Signal Processing and GNN-based Generative Modeling
• SoGCN: Second-Order Graph Convolutional Networks
• Optimization of Graph Neural Networks with Natural Gradient Descent
• DeepWukong
• mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
• A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials
• Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
• Convergent Privacy Framework for Multi-layer GNNs through Contractive Message Passing
• Reconstruction for Powerful Graph Representations
• Cell Complex Neural Networks
• DRIFT: Deep Reinforcement Learning for Functional Software Testing
• Pooling in Graph Convolutional Neural Networks
• Demystifying Distributed Training of Graph Neural Networks for Link Prediction
• Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
• Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
• Learning-aided Bigraph Matching Approach to Multi-Crew Restoration of Damaged Power Networks Coupled with Road Transportation Networks
• STAR: Sparse Transformer-based Action Recognition
• Federated Learning from Molecules to Processes: A Perspective
• Are Hyperbolic Representations in Graphs Created Equal?
• Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
• SliceGX: Layer-wise GNN Explanation with Model-slicing
• On Feature Normalization and Data Augmentation
• Over-squashing in Spatiotemporal Graph Neural Networks
• Construction of an Organ Shape Atlas Using a Hierarchical Mesh Variational Autoencoder
• Non-Overlap-Aware Egocentric Pose Estimation for Collaborative Perception in Connected Autonomy
• Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
• VersaGNN: a Versatile accelerator for Graph neural networks
• AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
• Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
• PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
• DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
• Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
• A Fair Comparison of Graph Neural Networks for Graph Classification
• INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
• ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
• Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
• HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
• Geometry-Aware Edge Pooling for Graph Neural Networks
• How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
• Subgraph Neural Networks
• Graph Semi-Supervised Learning for Point Classification on Data Manifolds
• Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
• Guided Graph Compression for Quantum Graph Neural Networks
• Benchmarking Quantum Architecture Search with Surrogate Assistance
• Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing
• Masked Language Models are Good Heterogeneous Graph Generalizers
• Simplicial Complex Representation Learning
• Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
• Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
• Large Language Models are Good Relational Learners
• Graph Neural Networks for Natural Language Processing: A Survey
• Graph Persistence goes Spectral
• Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning
• The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
• EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
• Positional Encoder Graph Neural Networks for Geographic Data
• Integrating Sensing and Communication in Cellular Networks via NR Sidelink
• Degree-Quant: Quantization-Aware Training for Graph Neural Networks
• Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
• Graph Convolutional Memory using Topological Priors
• Improving Graph Property Prediction with Generalized Readout Functions
• Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
• Reconstructing nodal pressures in water distribution systems with graph neural networks
• Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection
• Deep Learning on Graphs: A Survey
• Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
• Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
• Self-supervised Representation Learning for Evolutionary Neural Architecture Search
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
• BenLOC: A Benchmark for Learning to Configure MIP Optimizers
• Equivariant Subgraph Aggregation Networks
• RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases
• Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
• From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
• Hierarchical Inter-Message Passing for Learning on Molecular Graphs
• Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
• Probing Neural Topology of Large Language Models
• Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery
• Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
• DeGLIF for Label Noise Robust Node Classification using GNNs
• Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
• Point Cloud Instance Segmentation using Probabilistic Embeddings
• Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory
• Inductive Representation Learning on Temporal Graphs
• Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
• PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
• Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes
• Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter
• Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
• Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
• Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting
• Graph Classification by Mixture of Diverse Experts
• Directed Homophily-Aware Graph Neural Network
• Geoopt: Riemannian Optimization in PyTorch
• Learnable Kernel Density Estimation for Graphs
• Bridging Arbitrary and Tree Metrics via Differentiable Gromov Hyperbolicity
• Identifying Super Spreaders in Multilayer Networks
• Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks
• Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing
• xChemAgents: Agentic AI for Explainable Quantum Chemistry
• Graph Wave Networks
• PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints
• OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
• Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
• Learning for Dynamic Combinatorial Optimization without Training Data
• Information Obfuscation of Graph Neural Networks
• Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories
• Robustness questions the interpretability of graph neural networks: what to do?
• When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
• Convexified Message-Passing Graph Neural Networks
• Early-Exit Graph Neural Networks
• Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans
• On Graph Classification Networks, Datasets and Baselines
• Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling
• Learning Flexible Forward Trajectories for Masked Molecular Diffusion
• Joint Relational Database Generation via Graph-Conditional Diffusion Models
• An Empirical Study of Graph Contrastive Learning
• Efficient and Interpretable Robot Manipulation with Graph Neural Networks
• Survey of Image Based Graph Neural Networks
• Graph Data Selection for Domain Adaptation: A Model-Free Approach
• SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
• TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference
• Network Information Enhances Unreliable News Domain Detection
• p-Laplacian Based Graph Neural Networks
• Relationship Analysis of Image-Text Pair in SNS Posts
• A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data
• Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives
• Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
• Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
• Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
• RainfalLTE: A Zero-effect Rainfall Sensing System Utilizing Existing LTE Infrastructure
• Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
• DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• Self-Reinforced Graph Contrastive Learning
• Learning (Approximately) Equivariant Networks via Constrained Optimization
• RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
• Multi-Level Monte Carlo Training of Neural Operators
• How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection
• Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
• A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
• Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
• Neural Graduated Assignment for Maximum Common Edge Subgraphs
• Ripple: Scalable Incremental GNN Inferencing on Large Streaming Graphs
• Differentiable Lifting for Topological Neural Networks
• Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
• Deep Iterative Surface Normal Estimation
• ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model
• Space Group Equivariant Crystal Diffusion
• OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
• Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
• Graph Representational Learning: When Does More Expressivity Hurt Generalization?
• Relational Graph Transformer
• Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
• Conditional grain-graph diffusion for property-guided inverse design of polycrystalline microstructures
• GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics
• Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning
• Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
• Quotient Complex Transformer (QCformer) for Perovskite Data Analysis
• Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
• Fused3S: Fast Sparse Attention on Tensor Cores
• Towards a Taxonomy of Graph Learning Datasets
• Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis
• Wide & Deep Learning for Node Classification
• RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
• ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA
• Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
• Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer
• Graph Convolutional Networks for traffic anomaly
• Global Attention Improves Graph Networks Generalization
• TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
• Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
• Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
• Balancing Multi-level Interactions for Session-based Recommendation
• Semi-Supervised Deep Learning for Multiplex Networks
• MAGNET: an open-source library for mesh agglomeration by Graph Neural Networks
• Frequency-Space Mechanics: A Sequence and Coordinate-Free Representation for Protein Function Prediction
• Heterophily-informed Message Passing
• GraphNetz: Statistical Benchmarking of Graph Neural Networks with Paired Tests and Rank Aggregation
• Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
• Learning Hierarchical Interaction for Accurate Molecular Property Prediction
• Learning Efficiency Meets Symmetry Breaking
• PUFFIN: Protein Unit Discovery with Functional Supervision
• Efficient GNN Training Through Structure-Aware Randomized Mini-Batching
• Utilising Graph Machine Learning within Drug Discovery and Development
• Disentangling multispecific antibody function with graph neural networks
• Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
• Message Passing Query Embedding
• TAG-HGT: A Scalable and Cost-Effective Framework for Inductive Cold-Start Academic Recommendation
• Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection
• PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph
• Speaker Attribution with Voice Profiles by Graph-Based Semi-Supervised Learning
• ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing
• Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
• Benchmarking machine learning models for predicting aerofoil performance
• Optimization of a Triangular Delaunay Mesh Generator using Reinforcement Learning
• Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
• Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
• Scalable Graph Neural Networks for Heterogeneous Graphs
• Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks
• Fairness-Aware Node Representation Learning
• A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling
• 3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
• When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
• Scalable Graph Neural Network Training
• Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks
• Inference-friendly Graph Compression for Graph Neural Networks
• H³GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning
• The Impact of Dimensionality on the Stability of Node Embeddings
• Subset-Contrastive Multi-Omics Network Embedding
• Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
• Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
• Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs
• Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networks
• Leveraging GCN-based Action Recognition for Teleoperation in Daily Activity Assistance
• InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
• Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
• Scaling Graph Neural Networks for Particle Track Reconstruction
• Boosting Relational Deep Learning with Pretrained Tabular Models
• Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
• Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights
• MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage

Related

• PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs
• PyG 2.0: Scalable Learning on Real World Graphs
• pyGSL: A Graph Structure Learning Toolkit
• Learning distributed representations of graphs with Geo2DR
• PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network