- Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
- Stars: Tera-Scale Graph Building for Clustering and Learning
- GREED: A Neural Framework for Learning Graph Distance Functions
- Neural Approximation of Extended Persistent Homology on Graphs
- CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
- Recipe for a General, Powerful, Scalable Graph Transformer
- Hierarchical Graph Transformer with Adaptive Node Sampling
- NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
- Revisiting Heterophily For Graph Neural Networks
- Simplified Graph Convolution with Heterophily
- Decoupled Self-supervised Learning for Non-Homophilous Graphs
- Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
- Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again
- Not too little, not too much: a theoretical analysis of graph (over)smoothing
- Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?
- MGNNI: Multiscale Graph Neural Networks with Implicit Layers
- OPEN: Orthogonal Propagation with Ego-Network Modeling
- Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
- On the Robustness of Graph Neural Diffusion
- Are Defenses for Graph Neural Networks Robust?
- EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
- Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
- Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats
- Template based Graph Neural Network with Optimal Transport Distances
- GraphQNTK: the Quantum Neural Tangent Kernel for Graph Data
- Label-invariant Augmentation for Semi-Supervised Graph Classification
- Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
- Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
- Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
- Graph Scattering beyond Wavelet Shackles
- High-Order Pooling for Graph Neural Networks with Tensor Decomposition
- Graph Neural Networks with Adaptive Readouts
- Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
- Capturing Graphs with Hypo-Elliptic Diffusions
- Generalised Implicit Neural Representations
- Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
- Neuron with Steady Response Leads to Better Generalization
- Learning Invariant Graph Representations Under Distribution Shifts
- Towards Debiased Learning and Out-of-Distribution Detection for Graph Data
- Association Graph Learning for Multi-Task Classification with Category Shifts
- Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
- Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
- SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
- OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
- Explaining Graph Neural Networks with Structure-Aware Cooperative Games
- Task-Agnostic Graph Explanations
- Inherently Explainable Reinforcement Learning in Natural Language
- CLEAR: Generative Counterfactual Explanations on Graphs
- Equivariant Graph Hierarchy-based Neural Networks
- Equivariant Networks for Crystal Structures
- Learning Physical Dynamics with Subequivariant Graph Neural Networks
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
- How Powerful are K-hop Message Passing Graph Neural Networks
- A Practical, Progressively-Expressive GNN
- Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
- Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
- Geodesic Graph Neural Network for Efficient Graph Representation Learning
- Pure Transformers are Powerful Graph Learners
- Redundancy-Free Message Passing for Graph Neural Networks
- Universally Expressive Communication in Multi-Agent Reinforcement Learning
- Ordered Subgraph Aggregation Networks
- Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
- Provably expressive temporal graph networks
- Learning-based Manipulation Planning in Dynamic Environments Using GNNs and Temporal Encoding
- Learning Modular Simulations for Homogeneous Systems
- Sequential Latent Variable Models for Multiagent Trajectories
- Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
- Parameter-free Dynamic Graph Embedding for Link Prediction
- Learning interacting dynamical systems with latent Gaussian process ODEs
- Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
- Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
- AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs
- Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift
- Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
- Leaning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds
- Simultaneous Missing Value Imputation and Structure Learning with Groups
- On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs
- Geometric Distillation for Graph Networks
- Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks
- Interaction Modeling with Multiplex Attention
- ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
- Inductive Logical Query Answering in Knowledge Graphs
- Knowledge-Aware Bayesian Deep Topic Model
- Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graph
- Few-shot Relational Reasoning via Pretraining of Connection Subgraph Reconstruction
- Deep Bidirectional Language-Knowledge Pretraining
- Contrastive Language-Image Pre-Training with Knowledge Graphs
- Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
- Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks
- Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
- Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy
- DreamShard: Generalizable Embedding Table Placement for Recommender Systems
- SHINE: SubHypergraph Inductive Neural nEtwork
- Learning Enhanced Representation for Tabular Data via Neighborhood Propagation
- Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
- Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
- Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
- Uncovering the Structural Fairness in Graph Contrastive Learning
- Co-Modality Imbalanced Graph Contrastive Learning
- Generalized Laplacian Eigenmaps
- Contrastive Graph Structure Learning via Information Bottleneck for Recommendation
- Graph Self-supervised Learning with Accurate Discrepancy Learning
- S3GC: Scalable Self-Supervised Graph Clustering
- Understanding Self-Supervised Graph Representation Learning from a Data-Centric Perspective
- A Variational Edge Partition Model for Supervised Graph Representation Learning
- Evaluating Graph Generative Models with Contrastively Learned Features
- AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
- Deep Generative Model for Periodic Graphs
- Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
- ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
- Does GNN Pretraining Help Molecular Representation?
- Modular Flows: Differential Molecular Generation
- Molecule Generation by Principal Subgraph Mining and Assembling
- Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
- Regularized Molecular Conformation Fields
- Periodic Graph Transformers for Crystal Material Property Prediction
- So3krates - Self-attention for higher-order geometric interactions on arbitrary length-scales
- Spherical Channels for Modeling Atomic Interactions
- ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
- TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
- An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
- Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
- Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms
- MAgNet: Mesh Agnostic Neural PDE Solver
- M2N: Mesh Movement Networks for PDE Solvers
- Learning Interface Conditions in Domain Decomposition Solvers
- PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery
- Physical Design using Differentiable Learned Simulators
- Physics-Embedded Neural Networks: $\boldsymbol{\mathrm{E}(n)}$-Equivariant Graph Neural PDE Solvers
- Learning Rigid Body Dynamics with Lagrangian Graph Neural Network
- Graph Learning Assisted Multi-Objective Integer Programming
- Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
- A Deep Reinforcement Learning Framework for Column Generation
- Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
- DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
- Vision GNN: An Image is Worth Graph of Nodes
- Exact Shape Correspondence via 2D graph convolution
- Robust Graph Structure Learning over Images via Multiple Statistical Tests
- Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
- NSNet: A General Neural Probabilistic Framework for Satisfiability Problems
- Learning NP-Hard Joint-Assignment planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-iteration
- NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
- Unsupervised Learning for Combinatorial Optimization with Principled Objective Design
- Versatile Multi-stage Graph Neural Network for Circuit Representation
- Neural Topological Ordering for Computation Graphs
- Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
- Efficient Graph Similarity Computation with Alignment Regularization
- Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks
- $\textit{Public Wisdom Matters!}$ Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
- Pseudo-Riemannian Graph Convolutional Networks
- Graph Neural Network Bandits
- What Makes Graph Neural Networks Miscalibrated?
- First Hitting Diffusion Models
- coVariance Neural Networks
- Graph Neural Networks are Dynamic Programmers
- Graph Few-shot Learning with Task-specific Structures
- Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
- TA-GATES: An Encoding Scheme for Neural Network Architectures
- Iterative Structural Inference of Directed Graphs
- Symmetry-induced Disentanglement on Graphs
- A Fair Comparison of Two Popular Flat-Minima Optimizers: Stochastic Weight Averaging vs. Sharpness-Aware Minimization
- Transformers meet Stochastic Blockmodels: Attention with Data-Adaptive Sparsity and Cost