Neural Message Passing for Quantum Chemistry

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.

14 pages

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• Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
• Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
• GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
• Projective Ranking-based GNN Evasion Attacks
• A Survey on Graph Neural Networks for Knowledge Graph Completion
• Explaining Deep Graph Networks with Molecular Counterfactuals
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Stress and heat flux via automatic differentiation
• PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
• Provably Powerful Graph Networks
• NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
• Edge Representation Learning with Hypergraphs
• Beyond Graph Neural Networks with Lifted Relational Neural Networks
• Pointer Graph Networks
• Pre-training of Graph Augmented Transformers for Medication Recommendation
• PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
• How hard is to distinguish graphs with graph neural networks?
• Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
• Neural message passing for joint paratope-epitope prediction
• Learned Low Precision Graph Neural Networks
• End-to-End Entity Classification on Multimodal Knowledge Graphs
• Differentiable graph-structured models for inverse design of lattice materials
• PAN: Path Integral Based Convolution for Deep Graph Neural Networks
• Improving Graph Neural Networks with Simple Architecture Design
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
• Pre-training Graph Neural Networks with Kernels
• Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests
• Structure fusion based on graph convolutional networks for semi-supervised classification
• Set2Graph: Learning Graphs From Sets
• Graph Nets for Partial Charge Prediction
• What the foundations of quantum computer science teach us about chemistry
• Approximation Ratios of Graph Neural Networks for Combinatorial Problems
• BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation
• Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization
• Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
• Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
• Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
• RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
• Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
• Graph Neural Networks with Local Graph Parameters
• RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
• Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method
• Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift
• Energy-based View of Retrosynthesis
• Evaluating Logical Generalization in Graph Neural Networks
• Incomplete Graph Representation and Learning via Partial Graph Neural Networks
• Goal-directed graph construction using reinforcement learning
• Named Entity Disambiguation using Deep Learning on Graphs
• Graph Neural Networks for IceCube Signal Classification
• Recurrent Relational Networks
• Balancing Multi-level Interactions for Session-based Recommendation
• Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
• Graph Neural Network for Interpreting Task-fMRI Biomarkers
• On the Universality of Invariant Networks
• Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
• Large-scale graph representation learning with very deep GNNs and self-supervision
• HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
• Learning Intra-Batch Connections for Deep Metric Learning
• Parameterized Hypercomplex Graph Neural Networks for Graph Classification
• Variational Autoencoder for Anti-Cancer Drug Response Prediction
• Node Similarity Preserving Graph Convolutional Networks
• Equivariant Subgraph Aggregation Networks
• Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
• Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
• A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition
• Energy-based models for atomic-resolution protein conformations
• Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information
• Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
• Discrete-Valued Neural Communication
• Sampling methods for efficient training of graph convolutional networks: A survey
• Machine learning and invariant theory
• PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
• Text Level Graph Neural Network for Text Classification
• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
• Supervised Learning on Relational Databases with Graph Neural Networks
• Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
• Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration
• Factor Graph Neural Network
• Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
• Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
• Relation-based Motion Prediction using Traffic Scene Graphs
• A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
• Speaker attribution with voice profiles by graph-based semi-supervised learning
• Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
• Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
• Structural Inference of Networked Dynamical Systems with Universal Differential Equations
• Using Graph Neural Networks for Mass Spectrometry Prediction
• Distinguish Confusing Law Articles for Legal Judgment Prediction
• Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
• Memory-Based Graph Networks
• Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
• Discriminative structural graph classification
• Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
• Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
• Propagation Networks for Model-Based Control Under Partial Observation
• Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
• Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
• AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks
• Graph Generative Models for Fast Detector Simulations in High Energy Physics
• STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
• Global Attention Improves Graph Networks Generalization
• Graph Meta Learning via Local Subgraphs
• edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
• Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
• LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
• On Graph Classification Networks, Datasets and Baselines
• Dirichlet Graph Variational Autoencoder
• Neural Graph Embedding Methods for Natural Language Processing
• Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
• On the data-driven description of lattice materials mechanics
• Contrastive Graph Neural Network Explanation
• On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions
• A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
• Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
• Improving Compound Activity Classification via Deep Transfer and Representation Learning
• Directional Graph Networks
• On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
• Iterative Graph Self-Distillation
• Using Graph Neural Networks and Frequency Domain Data for Automated Operational Modal Analysis of Populations of Structures
• Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery
• Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks
• DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
• Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
• Frame Averaging for Invariant and Equivariant Network Design
• Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids
• Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
• Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
• Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
• IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
• Optimizing ZX-Diagrams with Deep Reinforcement Learning
• Efficient force field and energy emulation through partition of permutationally equivalent atoms
• Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
• You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
• Size-Invariant Graph Representations for Graph Classification Extrapolations
• Barking up the right tree: an approach to search over molecule synthesis DAGs
• Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
• Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
• Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
• How Framelets Enhance Graph Neural Networks
• Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
• Learning to Represent Programs with Heterogeneous Graphs
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
• Learning Graph-Level Representations with Recurrent Neural Networks
• VersaGNN: a Versatile accelerator for Graph neural networks
• From Local Structures to Size Generalization in Graph Neural Networks
• Detecting Beneficial Feature Interactions for Recommender Systems
• Search for light long-lived particles decaying to displaced jets in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV
• Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
• Improving Generative Imagination in Object-Centric World Models
• Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification
• High-quality Task Division for Large-scale Entity Alignment
• A Neural Network-based SAT-Resilient Obfuscation Towards Enhanced Logic Locking
• OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
• Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
• Hierarchical Human Parsing with Typed Part-Relation Reasoning
• Robust Line Segments Matching via Graph Convolution Networks
• MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
• EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
• A Generative Model for Molecular Distance Geometry
• Gated Graph Recursive Neural Networks for Molecular Property Prediction
• Constant Time Graph Neural Networks
• Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
• Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
• Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
• Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
• SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
• Enforcing Predictive Invariance across Structured Biomedical Domains
• GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
• Backpropagation through nonlinear units for all-optical training of neural networks
• Unveiling the hidden reaction kinetic network of carbon dioxide in supercritical aqueous solutions
• Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
• Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation
• Learning continuous-time PDEs from sparse data with graph neural networks
• Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
• PREDATOR: Registration of 3D Point Clouds with Low Overlap
• Information Obfuscation of Graph Neural Networks
• Learning and Interpreting Multi-Multi-Instance Learning Networks
• Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT
• Peptide-Spectra Matching from Weak Supervision
• The Impact of Global Structural Information in Graph Neural Networks Applications
• Artificial Intelligence in Drug Discovery: Applications and Techniques
• Molecule Property Prediction and Classification with Graph Hypernetworks
• Learning the Geodesic Embedding with Graph Neural Networks
• HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery
• Graph2Seq: Scalable Learning Dynamics for Graphs
• On the Representation of Solutions to Elliptic PDEs in Barron Spaces
• Simplifying Architecture Search for Graph Neural Network
• Neural Bipartite Matching
• MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
• Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
• A Hierarchy of Graph Neural Networks Based on Learnable Local Features
• Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
• Network In Graph Neural Network
• Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
• Are Graph Neural Networks Miscalibrated?
• Learning Representations of Missing Data for Predicting Patient Outcomes
• Self-supervised edge features for improved Graph Neural Network training
• FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
• MEMO: A Deep Network for Flexible Combination of Episodic Memories
• Small-footprint Keyword Spotting with Graph Convolutional Network
• Simplifying Clustering with Graph Neural Networks
• SDGNN: Learning Node Representation for Signed Directed Networks
• Learning Diverse Fashion Collocation by Neural Graph Filtering
• PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
• Multi-task Learning over Graph Structures
• Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
• Learning Robot Structure and Motion Embeddings using Graph Neural Networks
• Learning Domain-Independent Heuristics for Grounded and Lifted Planning
• Utilising Graph Machine Learning within Drug Discovery and Development
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
• Convolutional Geometric Matrix Completion
• LT-OCF: Learnable-Time ODE-based Collaborative Filtering
• Conditional Directed Graph Convolution for 3D Human Pose Estimation
• Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
• Relational Message Passing for Knowledge Graph Completion
• Graph neural induction of value iteration
• GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
• Modeling Attention Flow on Graphs
• Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
• Bipartite Graph Embedding via Mutual Information Maximization
• Generating Logical Forms from Graph Representations of Text and Entities
• Graph Neural Networks for Learning Robot Team Coordination
• Entropy-Transport distances between unbalanced metric measure spaces
• Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
• Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
• SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
• Unified Robust Training for Graph NeuralNetworks against Label Noise
• HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
• Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
• XLVIN: eXecuted Latent Value Iteration Nets
• Topology Aware Deep Learning for Wireless Network Optimization
• LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
• Trial by FIRE: Probing the dark matter density profile of dwarf galaxies with GraphNPE
• Particle Cloud Generation with Message Passing Generative Adversarial Networks
• On the equivalence of molecular graph convolution and molecular wave function with poor basis set
• Metapath- and Entity-aware Graph Neural Network for Recommendation
• Graph convolutions that can finally model local structure
• Attentional Multilabel Learning over Graphs: A Message Passing Approach
• Quaternion Graph Neural Networks
• Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
• GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
• Graph Convolution with Low-rank Learnable Local Filters
• A Hyperbolic-to-Hyperbolic Graph Convolutional Network
• Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
• Probabilistic Generative Deep Learning for Molecular Design
• DeepGG: a Deep Graph Generator
• Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
• Graph-Based Multi-Camera Soccer Player Tracker
• PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery
• The expressive power of kth-order invariant graph networks
• EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
• An Overview on the Application of Graph Neural Networks in Wireless Networks
• Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
• Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
• Semi-Supervised Learning on Graphs Based on Local Label Distributions
• Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks
• Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
• Edge Proposal Sets for Link Prediction
• Natural Language QA Approaches using Reasoning with External Knowledge
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
• Adversarial Graph Disentanglement
• Graph Homomorphism Convolution
• Attention-Based Learning on Molecular Ensembles
• Grammars and reinforcement learning for molecule optimization
• Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction
• SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics
• MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
• Graph Few-shot Learning via Knowledge Transfer
• Why Propagate Alone? Parallel Use of Labels and Features on Graphs
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
• Weisfeiler and Lehman Go Cellular: CW Networks
• From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
• Black-box Attacks Against Neural Binary Function Detection
• Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
• Teaching Temporal Logics to Neural Networks
• SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning
• PushNet: Efficient and Adaptive Neural Message Passing
• Learning Graph Neural Networks using Exact Compression
• Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
• Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
• Improving COVID-19 Forecasting using eXogenous Variables
• Improving Molecular Design by Stochastic Iterative Target Augmentation
• Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
• Learning Graph Structure With A Finite-State Automaton Layer
• DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
• Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network
• Next Waves in Veridical Network Embedding
• Equivariant Entity-Relationship Networks
• Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis
• Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers
• A Study of Joint Graph Inference and Forecasting
• Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation
• Graph Cross Networks with Vertex Infomax Pooling
• Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
• A Geometric Perspective on Visual Imitation Learning
• Feature Interaction-aware Graph Neural Networks
• Investigating 3D Atomic Environments for Enhanced QSAR
• Atomistic Graph Neural Networks for metals: Application to bcc iron
• Node Classification Meets Link Prediction on Knowledge Graphs
• CoSimGNN: Towards Large-scale Graph Similarity Computation
• Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
• Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
• Ego-based Entropy Measures for Structural Representations
• Reconstruction for Powerful Graph Representations
• The general theory of permutation equivarant neural networks and higher order graph variational encoders
• Neural Status Registers
• Sampling and Recovery of Graph Signals based on Graph Neural Networks
• Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
• Graph Sequential Network for Reasoning over Sequences
• Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
• Molecular distance matrix prediction based on graph convolutional networks
• Ab initio machine learning in chemical compound space
• A Dynamic Reduction Network for Point Clouds
• A comprehensive study on the prediction reliability of graph neural networks for virtual screening
• An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
• Collaborative Motion Prediction via Neural Motion Message Passing
• Accurately Solving Physical Systems with Graph Learning
• Complete Neural Networks for Complete Euclidean Graphs
• Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes
• Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
• Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
• Visual Semantic Information Pursuit: A Survey
• Finding spin glass ground states through deep reinforcement learning
• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
• Lossless Compression of Structured Convolutional Models via Lifting
• Hallucinating Optical Flow Features for Video Classification
• Continuous Graph Flow
• Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
• Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
• An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design
• XInsight: Revealing Model Insights for GNNs with Flow-based Explanations
• Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach
• Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions
• TransCamP: Graph Transformer for 6-DoF Camera Pose Estimation
• Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
• The Atlas for the Aspiring Network Scientist
• Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
• Learning Depthwise Separable Graph Convolution from Data Manifold
• Breaking the Expressive Bottlenecks of Graph Neural Networks
• Persistent Message Passing
• Node-Level Differentially Private Graph Neural Networks
• MPLP: Learning a Message Passing Learning Protocol
• Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
• MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions
• Graph Information Bottleneck for Subgraph Recognition
• Learning distributed representations of graphs with Geo2DR
• ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
• Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks
• From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
• Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding
• Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN
• Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
• Discrete Object Generation with Reversible Inductive Construction
• Exploiting Contextual Information with Deep Neural Networks
• Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
• Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
• Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer
• Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
• GraphITE: Estimating Individual Effects of Graph-structured Treatments
• Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
• Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
• GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
• Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
• Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
• Graph Neural Networks with Feature and Structure Aware Random Walk
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
• Neuralizing Efficient Higher-order Belief Propagation
• Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans
• Deep Generative Modeling in Network Science with Applications to Public Policy Research
• Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
• Data-Driven Learning of Geometric Scattering Networks
• gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs
• Zero-shot Synthesis with Group-Supervised Learning
• PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
• Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data
• Learning physics confers pose-sensitivity in structure-based virtual screening
• Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
• Unsupervised Resource Allocation with Graph Neural Networks
• Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
• Message Passing in Graph Convolution Networks via Adaptive Filter Banks
• Reliable Graph Neural Network Explanations Through Adversarial Training
• Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
• Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
• Adaptive Neural Message Passing for Inductive Learning on Hypergraphs
• Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning
• Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning
• Hybrid Low-order and Higher-order Graph Convolutional Networks
• Progressive Relation Learning for Group Activity Recognition
• Deep Data Flow Analysis
• Using ontology embeddings for structural inductive bias in gene expression data analysis
• SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors
• Cyclic Label Propagation for Graph Semi-supervised Learning
• Process-Level Representation of Scientific Protocols with Interactive Annotation
• Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction
• Signed Graph Attention Networks
• Time-Series Event Prediction with Evolutionary State Graph
• Drug Package Recommendation via Interaction-aware Graph Induction
• Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
• Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
• Should Graph Neural Networks Use Features, Edges, Or Both?
• Deep learning of material transport in complex neurite networks
• Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
• Quadratic GCN for Graph Classification
• Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions
• Geometrically Principled Connections in Graph Neural Networks
• Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
• Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
• Fast and Accurate: Structure Coherence Component for Face Alignment
• Scan2Mesh: From Unstructured Range Scans to 3D Meshes
• Graph2Kernel Grid-LSTM: A Multi-Cued Model for Pedestrian Trajectory Prediction by Learning Adaptive Neighborhoods
• Robust Hierarchical Graph Classification with Subgraph Attention
• Message Passing Graph Kernels
• Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network
• CoulGAT: An Experiment on Interpretability of Graph Attention Networks
• Relational State-Space Model for Stochastic Multi-Object Systems
• BayesGrad: Explaining Predictions of Graph Convolutional Networks
• Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
• On the Universality of Graph Neural Networks on Large Random Graphs
• Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
• Neural Enhanced Belief Propagation for Cooperative Localization
• Computing Steiner Trees using Graph Neural Networks
• Data-Centric AI Requires Rethinking Data Notion
• Region-based Energy Neural Network for Approximate Inference
• Spectral Network Embedding: A Fast and Scalable Method via Sparsity
• Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
• Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory
• Optimal message passing for molecular prediction is simple, attentive and spatial
• PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
• Graph Analysis and Graph Pooling in the Spatial Domain
• Structural Optimization Makes Graph Classification Simpler and Better
• AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
• Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
• Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
• Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
• Neural Circuit Synthesis from Specification Patterns
• InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
• struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
• Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
• Rotation Averaging with Attention Graph Neural Networks
• A Spectral Nonlocal Block for Neural Networks
• Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
• Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
• Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
• Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
• HOPF: Higher Order Propagation Framework for Deep Collective Classification
• Graph Denoising with Framelet Regularizer
• Neural Trees for Learning on Graphs
• Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
• On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
• Predicting outcomes of catalytic reactions using machine learning
• Graph-FCN for image semantic segmentation
• A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
• LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
• Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
• Reasoning-Modulated Representations
• Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction
• NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
• GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end
• Compositional Language Understanding with Text-based Relational Reasoning
• Automatic Text Extractive Summarization Based on Graph and Pre-trained Language Model Attention
• Fast OBDD Reordering using Neural Message Passing on Hypergraph
• Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
• Vitruvion: A Generative Model of Parametric CAD Sketches
• Improving the Long-Range Performance of Gated Graph Neural Networks
• Deep Constraint-based Propagation in Graph Neural Networks
• Graph Filtration Learning
• Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
• Beyond permutation equivariance in graph networks
• Node Feature Kernels Increase Graph Convolutional Network Robustness
• Lorentzian Graph Convolutional Networks
• Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
• Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
• Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
• Relational dynamic memory networks
• Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks
• Stable Prediction on Graphs with Agnostic Distribution Shift
• End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks
• Sparse hierarchical representation learning on molecular graphs
• Personalized Hashtag Recommendation for Micro-videos
• TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
• Partition and Code: learning how to compress graphs
• Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization
• Fast Haar Transforms for Graph Neural Networks
• Heterogeneous Graph Neural Network with Multi-view Representation Learning
• ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
• Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
• Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
• Logic and the $2$-Simplicial Transformer
• VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization
• Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping
• RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
• Stateless actor-critic for instance segmentation with high-level priors
• Deep Message Passing on Sets
• Equivariant Neural Network for Factor Graphs
• A Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse ansatz via Machine Learning and Cheminformatics
• On the Robustness of Deep Learning-predicted Contention Models for Network Calculus
• Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds
• Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
• Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
• Speaker diarization with session-level speaker embedding refinement using graph neural networks
• Analysis of Atomistic Representations Using Weighted Skip-Connections
• CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
• An FEA surrogate model with Boundary Oriented Graph Embedding approach
• Graph Neural Network for Hamiltonian-Based Material Property Prediction
• SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
• SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features
• Isometric Graph Neural Networks
• Convolutions for Spatial Interaction Modeling
• Signed Bipartite Graph Neural Networks
• Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
• STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction
• Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
• Node Copying for Protection Against Graph Neural Network Topology Attacks
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
• Graph-Based Social Relation Reasoning
• OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
• Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
• Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
• Graph Convolutional Memory using Topological Priors
• Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
• Improving accuracy of interatomic potentials: more physics or more data? A case study of silica
• Jointly learning relevant subgraph patterns and nonlinear models of their indicators
• Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
• Symmetry-driven graph neural networks
• Docking-based Virtual Screening with Multi-Task Learning
• Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line-Specific Synergistic Drug Combinations
• Irregular Convolutional Auto-Encoder on Point Clouds
• Improving Graph Property Prediction with Generalized Readout Functions
• Graph Feature Gating Networks
• Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
• ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks
• Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
• On the Global Self-attention Mechanism for Graph Convolutional Networks
• Multi-fidelity Stability for Graph Representation Learning
• Rotation Equivariant 3D Hand Mesh Generation from a Single RGB Image
• Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
• Network representation learning: A macro and micro view
• Image-Like Graph Representations for Improved Molecular Property Prediction
• Subspace Graph Physics: Real-Time Rigid Body-Driven Granular Flow Simulation
• Spectral Transform Forms Scalable Transformer
• From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
• Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs
• PotentialNet for Molecular Property Prediction
• Generating Symbolic Reasoning Problems with Transformer GANs
• Barlow Graph Auto-Encoder for Unsupervised Network Embedding
• Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks
• Neural representation and generation for RNA secondary structures
• Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
• Graphs for deep learning representations
• Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification
• Sequential Graph Dependency Parser