Neural Message Passing for Quantum Chemistry

2017/04/04 by Justin Gilmer, Gilmer, Justin, Samuel S. Schoenholz +8 · 873 citations
Chemistry · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Various Chemistry Research Topics #cs.LG

paper · pdf · doi:10.48550/arxiv.1704.01212

14 pages

arxiv created 2017/06/12 · arxiv updated 2017/06/14

Abstract

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.

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• Over-Squashing in GNNs and Causal Inference of Rewiring Strategies
• Exact Verification of Graph Neural Networks with Incremental Constraint Solving
• Hybrid Node-Destroyer Model with Large Neighborhood Search for Solving the Capacitated Vehicle Routing Problem
• Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey
• Chi-Geometry: A Library for Benchmarking Chirality Prediction of GNNs
• Multi-View Graph Neural Networks for Molecular Property Prediction
• Generalization and Representational Limits of Graph Neural Networks
• Graph Neural Networks with Generated Parameters for Relation Extraction
• MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
• Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
• Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
• Graphite: Iterative Generative Modeling of Graphs
• Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning
• Geometry-Aware Spiking Graph Neural Network
• Progressive Relation Learning for Group Activity Recognition
• Discriminative structural graph classification
• Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
• Hypergraph Neural Network with State Space Models for Node Classification
• Aggregate-Combine-Readout GNNs Are More Expressive Than Logic C2
• Constant Curvature Graph Convolutional Networks
• Multi-Stage Knowledge-Distilled VGAE and GAT for Robust Controller-Area-Network Intrusion Detection
• A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation
• Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
• Adversarial Graph Augmentation to Improve Graph Contrastive Learning
• Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
• Online Continual Graph Learning
• Learned Low Precision Graph Neural Networks
• Adaptive Riemannian Graph Neural Networks
• PyG 2.0: Scalable Learning on Real World Graphs
• Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
• Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
• Invariant Graph Transformer for Out-of-Distribution Generalization
• Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
• MPFSR-Enhanced GNNs: Spectral Graph Neural Networks Enhancement Through Learnable Multiple-Parameter Graph Fractional Fourier Transforms
• Boosting Team Modeling through Tempo-Relational Representation Learning
• Using ontology embeddings for structural inductive bias in gene expression data analysis
• MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
• BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
• Generative Code Modeling with Graphs
• Strong Generalization and Efficiency in Neural Programs
• Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
• Active Learning for Graph Neural Networks via Node Feature Propagation
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
• End-to-End Entity Classification on Multimodal Knowledge Graphs
• Stealing Links from Graph Neural Networks
• Lossless Compression of Structured Convolutional Models via Lifting
• Reasoning-Modulated Representations
• Graph Information Bottleneck
• Neural Graph Embedding Methods for Natural Language Processing
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
• A Hierarchy of Graph Neural Networks Based on Learnable Local Features
• Iterative Graph Self-Distillation
• KD-GAT: Combining Knowledge Distillation and Graph Attention Transformer for a Controller Area Network Intrusion Detection System
• Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
• Learning Long-Range Representations with Equivariant Messages
• Geometric Multi-color Message Passing Graph Neural Networks for Blood-brain Barrier Permeability Prediction
• Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
• Discrete Object Generation with Reversible Inductive Construction
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
• FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
• ARTreeFormer: A Faster Attention-based Autoregressive Model for Phylogenetic Inference
• A Gentle Introduction to Deep Learning for Graphs
• STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction
• Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks
• Principal Neighbourhood Aggregation for Graph Nets
• Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
• Research on the application of graph data structure and graph neural network in node classification/clustering tasks
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
• IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
• Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
• Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
• Teaching Temporal Logics to Neural Networks
• Breaking the Expressive Bottlenecks of Graph Neural Networks
• Attention-Based Learning on Molecular Ensembles
• The emergence of machine learning force fields in drug design
• Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
• Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning
• OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
• Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes
• Robust Anomaly Detection with Graph Neural Networks using Controllability
• Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
• Graph Neural Networks with Feature and Structure Aware Random Walk
• Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
• Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
• Distilling Knowledge from Graph Convolutional Networks
• Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
• Symbolic Graph Intelligence: Hypervector Message Passing for Learning Graph-Level Patterns with Tsetlin Machines
• Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
• Wasserstein Hypergraph Neural Network
• RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
• Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
• Beyond permutation equivariance in graph networks
• Exploiting Contextual Information with Deep Neural Networks
• Robust Line Segments Matching via Graph Convolution Networks
• CCNet: Criss-Cross Attention for Semantic Segmentation
• Improving Graph Neural Networks with Simple Architecture Design
• Pre-training of Graph Augmented Transformers for Medication Recommendation
• Learning Graph-Level Representations with Recurrent Neural Networks
• RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
• NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
• Hybrid Low-order and Higher-order Graph Convolutional Networks
• Neural Enhanced Belief Propagation for Cooperative Localization
• Region-based Energy Neural Network for Approximate Inference
• TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
• Graph Star Net for Generalized Multi-Task Learning
• Collaborative Motion Prediction via Neural Motion Message Passing
• Effects of relational graph modularity and depth on the learning performance of neural networks
• A Pre-training Framework for Relational Data with Information-theoretic Principles
• The Shape of Deceit: Behavioral Consistency and Fragility in Money Laundering Patterns
• Generating valid Euclidean distance matrices
• Unified People Tracking with Graph Neural Networks
• Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
• Grounding Methods for Neural-Symbolic AI
• Consciousness as a Jamming Phase
• Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
• SILVA Networks as Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields
• DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
• MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
• Generating Symbolic Reasoning Problems with Transformer GANs
• Sinkhorn Normalization of Diffusion Kernels
• Robust Hierarchical Graph Classification with Subgraph Attention
• Self-Supervised Graph Transformer on Large-Scale Molecular Data
• Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
• Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing
• MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding
• Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
• Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
• Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
• Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
• Symmetry-driven graph neural networks
• Multi-Level Fusion Graph Neural Network for Molecule Property Prediction
• Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization
• MolVision: Molecular Property Prediction with Vision Language Models
• CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
• Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
• MILP-SAT-GNN: Yet Another Neural SAT Solver
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
• Geometrically Principled Connections in Graph Neural Networks
• Understanding Generalization in Node and Link Prediction
• NN-Former: Rethinking Graph Structure in Neural Architecture Representation
• A Spectral Nonlocal Block for Neural Networks
• Cooperative Sheaf Neural Networks
• Rotational Sampling: A Plug-and-Play Encoder for Rotation-Invariant 3D Molecular GNNs
• Transaction Categorization with Relational Deep Learning in QuickBooks
• When GNNs Met a Word Equations Solver: Learning to Rank Equations (Extended Technical Report)
• Hybrid intelligence for environmental pollution: biodegradability assessment of organic compounds through multimodal integration of graph attention networks and QSAR models
• Contrastive Graph Neural Network Explanation
• Process-Level Representation of Scientific Protocols with Interactive Annotation
• A unified framework for establishing the universal approximation of transformer-type architectures
• Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
• Reconstruction for Powerful Graph Representations
• Irregular Convolutional Auto-Encoder on Point Clouds
• Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
• Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
• From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
• Node Copying for Protection Against Graph Neural Network Topology Attacks
• MolProphecy: Bridging Medicinal Chemists' Knowledge and Molecular Pre-Trained Models via a Multi-Modal Framework
• Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
• Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
• Directed Link Prediction using GNN with Local and Global Feature Fusion
• A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
• Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
• Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
• Federated Learning from Molecules to Processes: A Perspective
• Towards a deeper GCN: Alleviate over-smoothing with iterative training and fine-tuning
• MEMO: A Deep Network for Flexible Combination of Episodic Memories
• Mesh-Informed Neural Operator : A Transformer Generative Approach
• Heterogeneous Graph Neural Networks for Extractive Document Summarization
• Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
• Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
• CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
• Atomistic Graph Neural Networks for metals: Application to bcc iron
• SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics
• Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
• Over-squashing in Spatiotemporal Graph Neural Networks
• Reasoning Visual Dialogs with Structural and Partial Observations
• A Two-Step Graph Convolutional Decoder for Molecule Generation
• Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
• Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks
• Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
• Spectral Clustering with Graph Neural Networks for Graph Pooling
• Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
• Learning geometric invariant features for classification of vector polygons with graph message-passing neural network
• DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
• A Survey on Graph Neural Networks for Knowledge Graph Completion
• Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
• SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features
• Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table
• Graph Neural Network for Traffic Forecasting: A Survey
• How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
• Subgraph Neural Networks
• FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
• Graph Semi-Supervised Learning for Point Classification on Data Manifolds
• pLSTM: parallelizable Linear Source Transition Mark networks
• KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity
• Sampling methods for efficient training of graph convolutional networks: A survey
• Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data
• Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization
• Learning to Represent Programs with Heterogeneous Graphs
• GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras
• Caterpillar GNN: Replacing Message Passing with Efficient Aggregation
• All SMILES Variational Autoencoder
• Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition
• Positional Encoding meets Persistent Homology on Graphs
• 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
• On Measuring Long-Range Interactions in Graph Neural Networks
• Flow-Attentional Graph Neural Networks
• Graph Persistence goes Spectral
• Rapid training of Hamiltonian graph networks using random features
• Detecting Beneficial Feature Interactions for Recommender Systems
• Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
• chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
• Why Propagate Alone? Parallel Use of Labels and Features on Graphs
• A Very Deep Graph Convolutional Network for13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment
• HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
• Graph Convolution with Low-rank Learnable Local Filters
• Piecewise Constant Spectral Graph Neural Network
• Stable Prediction on Graphs with Agnostic Distribution Shift
• Logic and the 2-Simplicial Transformer
• A Survey on The Expressive Power of Graph Neural Networks
• GraphITE: Estimating Individual Effects of Graph-structured Treatments
• Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
• Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• Convolutional Geometric Matrix Completion
• Graph Convolutional Memory using Topological Priors
• Navigating Chemical Space: Multi-Level Bayesian Optimization with Hierarchical Coarse-Graining
• Multi-domain anomaly detection in a 5G network
• SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
• On the Universality of Invariant Networks
• Deep Learning on Graphs: A Survey
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
• HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
• Equivariant Subgraph Aggregation Networks
• Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
• Principled Data Augmentation for Learning to Solve Quadratic Programming Problems
• Neural representation and generation for RNA secondary structures
• Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training
• ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
• Unlearning Inversion Attacks for Graph Neural Networks
• Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance
• Graph Sequential Network for Reasoning over Sequences
• Bias as a Virtue: Rethinking Generalization under Distribution Shifts
• Unsupervised Resource Allocation with Graph Neural Networks
• Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
• Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
• STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
• Neural Trees for Learning on Graphs
• GrapheonRL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems
• WILTing Trees: Interpreting the Distance Between MPNN Embeddings
• AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
• Representing local protein environments with machine learning force fields
• Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
• 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
• A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
• Primal-Dual Neural Algorithmic Reasoning
• The Generalized Skew Spectrum of Graphs
• DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
• GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
• Machine-Learned Potentials for Solvation Modeling
• FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design
• Geometric Hyena Networks for Large-scale Equivariant Learning
• Directed Homophily-Aware Graph Neural Network
• Continuous Graph Flow
• Learnable Kernel Density Estimation for Graphs
• Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
• GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
• CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning
• Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
• Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
• Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing
• Simple yet Effective Graph Distillation via Clustering
• OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
• Information Obfuscation of Graph Neural Networks
• Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training
• Tokenizing Electron Cloud in Protein-Ligand Interaction Learning
• SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
• Chordless Structure: A Pathway to Simple and Expressive GNNs
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
• Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
• STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
• Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
• Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
• How Particle System Theory Enhances Hypergraph Message Passing
• Graph Filtration Learning
• AbBiBench: A Benchmark for Antibody Binding Affinity Maturation and Design
• Convexified Message-Passing Graph Neural Networks
• Early-Exit Graph Neural Networks
• An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
• CoRe-GNN: Multilevel Message passing on Coarsened graphs
• DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
• Discrete-Valued Neural Communication
• Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding
• The general theory of permutation equivarant neural networks and higher order graph variational encoders
• On Graph Classification Networks, Datasets and Baselines
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
• Gated Graph Recursive Neural Networks for Molecular Property Prediction
• Energy-based View of Retrosynthesis
• Learning Graph Structure With A Finite-State Automaton Layer
• Joint Relational Database Generation via Graph-Conditional Diffusion Models
• Generative Graph Pattern Machine
• Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
• Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
• Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
• Deep Message Passing on Sets
• ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
• Directional Non-Commutative Monoidal Structures for Compositional Embeddings in Machine Learning
• Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
• Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
• HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
• Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
• Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
• PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
• Nonparametric Teaching for Graph Property Learners
• DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
• Learning (Approximately) Equivariant Networks via Constrained Optimization
• Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
• Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
• A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
• Neural Graduated Assignment for Maximum Common Edge Subgraphs
• Structured Relational Representations
• Differentiable Lifting for Topological Neural Networks
• The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
• Finding Counterfactual Evidences for Node Classification
• Halting Recurrent GNNs and the Graded μ-Calculus
• OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
• Accelerating Parameter Initialization in Quantum Chemical Simulations via LSTM-FC-VQE
• Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
• Graph Representational Learning: When Does More Expressivity Hurt Generalization?
• RapidGNN: Communication Efficient Large-Scale Distributed Training of Graph Neural Networks
• Relational Graph Transformer
• Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping
• GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics
• Negative Metric Learning for Graphs
• Physical regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction
• Learning Repetition-Invariant Representations for Polymer Informatics
• Schreier-Coset Graph Propagation
• Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning
• Data-Driven Learning of Geometric Scattering Networks
• A Study of Joint Graph Inference and Forecasting
• Signed Bipartite Graph Neural Networks
• CodeJeNN: A simple C++ neural network generator for physics applications
• Structured Neural Summarization
• Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
• Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
• The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
• Molecule Property Prediction and Classification with Graph Hypernetworks
• Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
• Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation
• Wide & Deep Learning for Node Classification
• Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
• MolQAE: Quantum Autoencoder for Molecular Representation Learning
• Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
• In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts
• Learning Diverse Fashion Collocation by Neural Graph Filtering
• edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
• SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
• Supervised Learning on Relational Databases with Graph Neural Networks
• Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction
• Understanding Truncated Positional Encodings for Graph Neural Networks
• Global Attention Improves Graph Networks Generalization
• Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures
• A Unified Lottery Ticket Hypothesis for Graph Neural Networks
• Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
• Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
• Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks
• Repetition Makes Perfect: Recurrent Graph Neural Networks Match Message-Passing Limit
• Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contribution for Molecular Odor Prediction
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
• Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
• Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding
• A Hyperbolic-to-Hyperbolic Graph Convolutional Network
• Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
• Cyclic Label Propagation for Graph Semi-supervised Learning
• Learning and Interpreting Multi-Multi-Instance Learning Networks
• Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
• CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
• Hallucinating Optical Flow Features for Video Classification
• Network In Graph Neural Network
• Balancing Multi-level Interactions for Session-based Recommendation
• Unifying approach to uniform expressivity of graph neural networks
• Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction
• Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
• Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
• UniField: RBF-Guided Electron Density Fusion for Enhanced Molecular Representations
• MoleCode unlocks structural intelligence in large language models
• Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
• Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation
• Learning Laplacian Positional Encodings for Heterophilous Graphs
• Understanding GNNs and Homophily in Dynamic Node Classification
• Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
• Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
• SMILES all around: structure to SMILES conversion for transition metal complexes
• Learning Hierarchical Interaction for Accurate Molecular Property Prediction
• Utilising Graph Machine Learning within Drug Discovery and Development
• RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine
• Disentangling multispecific antibody function with graph neural networks
• Reliable Graph Neural Network Explanations Through Adversarial Training
• Graph Kernels: State-of-the-Art and Future Challenges
• Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease
• Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
• Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
• When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index
• NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning
• FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks
• Patients-like-me: A Variational LM--GNN Framework for Explainable Clinical Prediction
• Combining Machine Learning and Physics to Understand Glassy Systems
• Speaker Attribution with Voice Profiles by Graph-Based Semi-Supervised Learning
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions
• Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks
• Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks
• Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations
• PushNet: Efficient and Adaptive Neural Message Passing
• Representation, learning, and planning algorithms for geometric task and motion planning
• Structure Fusion Based on Graph Convolutional Networks for Node Classification in Citation Networks
• Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
• Natural Graph Networks
• Text Level Graph Neural Network for Text Classification
• A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling
• Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
• Natural Language QA Approaches using Reasoning with External Knowledge
• Molecular distance matrix prediction based on graph convolutional networks
• LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
• Relational State-Space Model for Stochastic Multi-Object Systems
• Machine learning on neutron and x-ray scattering and spectroscopies
• Structural Optimization Makes Graph Classification Simpler and Better
• Memory-Based Graph Networks
• Ego-based Entropy Measures for Structural Representations
• Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
• Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment
• Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems
• Weight-of-Thought Reasoning: Exploring Neural Network Weights for Enhanced LLM Reasoning
• AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability
• Spatially Directional Dual-Attention GAT for Spatial Fluoride Health Risk Modeling
• Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networks
• GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction
• SAFT: Structure-aware Transformers for Textual Interaction Classification
• Boosting Relational Deep Learning with Pretrained Tabular Models
• Using attribution to decode binding mechanism in neural network models for chemistry

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