- Alternately Optimized Graph Neural Networks
- Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
- {L}azy{GNN}: Large-Scale Graph Neural Networks via Lazy Propagation
- Graph Ladling: Shockingly Simple Parallel {GNN} Training without Intermediate Communication
- Graph Neural Tangent Kernel: Convergence on Large Graphs
- {RSC}: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
- {GOAT}: A Global Transformer on Large-scale Graphs
- On the Connection Between {MPNN} and Graph Transformer
- Graph Generative Model for Benchmarking Graph Neural Networks
- Transformers Meet Directed Graphs
- Graph Inductive Biases in Transformers without Message Passing
- Exphormer: Sparse Transformers for Graphs
- A Generalization of {V}i{T}/{MLP}-Mixer to Graphs
- {DR}ew: Dynamically Rewired Message Passing with Delay
- Implicit Graph Neural Networks: A Monotone Operator Viewpoint
- Ewald-based Long-Range Message Passing for Molecular Graphs
- On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
- Understanding Oversquashing in {GNN}s through the Lens of Effective Resistance
- Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
- Improving Graph Neural Networks with Learnable Propagation Operators
- Half-Hop: A graph upsampling approach for slowing down message passing
- Towards Deep Attention in Graph Neural Networks: Problems and Remedies
- {GRAFENNE}: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
- Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
- {WL} meet {VC}
- Local Vertex Colouring Graph Neural Networks
- On the Expressive Power of Geometric Graph Neural Networks
- From Relational Pooling to Subgraph {GNN}s: A Universal Framework for More Expressive Graph Neural Networks
- Expectation-Complete Graph Representations with Homomorphisms
- Path Neural Networks: Expressive and Accurate Graph Neural Networks
- A Complete Expressiveness Hierarchy for Subgraph {GNN}s via Subgraph Weisfeiler-Lehman Tests
- Graph Positional Encoding via Random Feature Propagation
- Efficient and Equivariant Graph Networks for Predicting Quantum {H}amiltonian
- Equivariant Polynomials for Graph Neural Networks
- E$(n)$ Equivariant Message Passing Simplicial Networks
- Multi-Layer Neural Networks as Trainable Ladders of {H}ilbert Spaces
- End-to-End Full-Atom Antibody Design
- Reducing {SO}(3) Convolutions to {SO}(2) for Efficient Equivariant {GNN}s
- {FAEN}et: Frame Averaging Equivariant {GNN} for Materials Modeling
- Towards Understanding Generalization of Graph Neural Networks
- {F}isher Information Embedding for Node and Graph Learning
- Structural Re-weighting Improves Graph Domain Adaptation
- {W}asserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
- Uncertainty Estimation for Molecules: Desiderata and Methods
- Generalizing Neural Wave Functions
- {F}usion{R}etro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
- A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
- Efficiently predicting high resolution mass spectra with graph neural networks
- Von Mises Mixture Distributions for Molecular Conformation Generation
- Conditional Graph Information Bottleneck for Molecular Relational Learning
- Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
- Metagenomic Binning using Connectivity-constrained Variational Autoencoders
- Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
- {S}trider{N}et: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
- Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
- {TIDE}: Time Derivative Diffusion for Deep Learning on Graphs
- {GREAD}: Graph Neural Reaction-Diffusion Networks
- Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
- Graphically Structured Diffusion Models
- Autoregressive Diffusion Model for Graph Generation
- {RGE}: A Repulsive Graph Rectification for Node Classification via Influence
- Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
- Conformal Prediction Sets for Graph Neural Networks
- Distribution Free Prediction Sets for Node Classification
- Linkless Link Prediction via Relational Distillation
- Quantifying the Knowledge in {GNN}s for Reliable Distillation into {MLP}s
- Graph Contrastive Backdoor Attacks
- Boosting Graph Contrastive Learning via Graph Contrastive Saliency
- When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
- Randomized Schur Complement Views for Graph Contrastive Learning
- {SEGA}: Structural Entropy Guided Anchor View for Graph Contrastive Learning
- {C}o{C}o: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
- Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
- Rethinking Weak Supervision in Helping Contrastive Learning
- {S}lot{GAT}: Slot-based Message Passing for Heterogeneous Graphs
- Disentangled Multiplex Graph Representation Learning
- {I}n{G}ram: Inductive Knowledge Graph Embedding via Relation Graphs
- What Makes Entities Similar? {A} Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
- Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
- Contrastive Learning Meets Homophily: Two Birds with One Stone
- Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
- Dink-Net: Neural Clustering on Large Graphs
- Total Variation Graph Neural Networks
- {GC}-Flow: A Graph-Based Flow Network for Effective Clustering
- Featured Graph Coarsening with Similarity Guarantees
- A Gromov-{W}asserstein Geometric View of Spectrum-Preserving Graph Coarsening
- Graph Neural Networks with Learnable and Optimal Polynomial Bases
- Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
- Relevant Walk Search for Explaining Graph Neural Networks
- Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
- {D}2{M}atch: Leveraging Deep Learning and Degeneracy for Subgraph Matching
- {S}eed{GNN}: Graph Neural Network for Supervised Seeded Graph Matching
- Graph Reinforcement Learning for Network Control via Bi-Level Optimization
- Towards Robust Graph Incremental Learning on Evolving Graphs
- Modeling Dynamic Environments with Scene Graph Memory
- Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
- Learning Preconditioners for Conjugate Gradient {PDE} Solvers
- Training Deep Surrogate Models with Large Scale Online Learning
- {A}b{ODE}: Ab initio antibody design using conjoined {ODE}s
- {MG}-{GNN}: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
- Towards Understanding and Reducing Graph Structural Noise for {GNN}s
- {G}raph{C}leaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
- Graph Mixup with Soft Alignments
- Generated Graph Detection
- On Strengthening and Defending Graph Reconstruction Attack with {M}arkov Chain Approximation
- Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
- Personalized Subgraph Federated Learning
- Vertical Federated Graph Neural Network for Recommender System
- Personalized Federated Learning with Inferred Collaboration Graphs
- Smart Initial Basis Selection for Linear Programs
- {GNN}&{GBDT}-Guided Fast Optimizing Framework for Large-scale Integer Programming
- Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
- Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
- Deep Graph Representation Learning and Optimization for Influence Maximization
- Universal Morphology Control via Contextual Modulation
- Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum {QAP} Solver
- Generative Graph Dictionary Learning
- All in a Row: Compressed Convolution Networks for Graphs
- Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
- Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
- From Hypergraph Energy Functions to Hypergraph Neural Networks
- {L}in{SATN}et: The Positive Linear Satisfiability Neural Networks
- When and How Does Known Class Help Discover Unknown Ones? {P}rovable Understanding Through Spectral Analysis
- Neural Algorithmic Reasoning with Causal Regularisation
- On the Initialization of Graph Neural Networks
- Improving Graph Generation by Restricting Graph Bandwidth
- Node Embedding from Neural {H}amiltonian Orbits in Graph Neural Networks
- Neural Status Registers
- Feature Expansion for Graph Neural Networks
- Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
- Learning Representations without Compositional Assumptions
- Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
- {B}ayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
- {S}ke2{G}rid: Skeleton-to-Grid Representation Learning for Action Recognition