- TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
- Nonlinear Feature Diffusion on Hypergraphs
- Structural Entropy Guided Graph Hierarchical Pooling
- A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
- The Infinite Contextual Graph Markov Model
- G-Mixup: Graph Data Augmentation for Graph Classification
- Learning from Counterfactual Links for Link Prediction
- Local Augmentation for Graph Neural Networks
- GraphFM: Improving Large-Scale GNN Training via Feature Momentum
- Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
- Scalable Deep Gaussian Markov Random Fields for General Graphs
- LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
- Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
- Information Bottleneck-Guided Stochastic Attention Mechanism for Interpretable Graph Learning
- Optimization-induced Implicit Graph Diffusion
- pathGCN: Learning General Graph Spatial Operators from Paths
- NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
- Graph-Coupled Oscillator Networks
- Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
- $p$-Laplacian Based Graph Neural Networks
- G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters
- Convergence of Invariant Graph Networks
- A New Perspective on the Effects of Spectrum in Graph Neural Networks
- How Powerful are Spectral Graph Neural Networks
- SPECTRE : Spectral Conditioning Overcomes the Expressivity Limits of One-shot Graph Generators
- A Theoretical Comparison of Graph Neural Network Extensions
- Boosting Graph Structure Learning with Dummy Nodes
- Weisfeiler-Lehman meets Gromov-Wasserstein
- Going Deeper into Permutation-Sensitive Graph Neural Networks
- DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
- On the Equivalence Between Temporal and Static Equivariant Graph Representations
- Equivariant graph neural networks with complete local frames
- Equivariant Quantum Graph Circuits
- SpeqNets: Sparsity-aware permutation-equivariant graph networks
- 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
- 3D Infomax improves GNNs for Molecular Property Prediction
- Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction
- Generative Coarse-Graining of Molecular Conformations
- Equivariant Diffusion for Molecule Generation in 3D
- Generating 3D Molecules for Target Protein Binding
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
- Antibody-Antigen Interface Design via Hierarchical Structure Refinement
- VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
- Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
- Structure-Aware Transformer for Graph Representation Learning
- Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer
- From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
- Neural-Symbolic Models for Logical Queries on Knowledge Graphs
- Cycle Representation Learning for Inductive Relation Prediction
- Semiparametric Subgraph Reasoning for Question Answering over Large Knowledge Bases
- Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
- Self-Supervised Representation Learning via Latent Graph Prediction
- ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
- Augment with Care: Contrastive Learning for Combinatorial Problems
- Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
- Cross-Space Active Learning on Graph Convolutional Networks
- GALAXY: Graph-based Active Learning at the Extreme
- Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
- Optimizing Tensor Network Contraction Using Reinforcement Learning
- Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
- Learning to Assemble with Large-Scale Structured Reinforcement Learning
- Constraint-based graph network simulator
- Learning to Solve PDE-constrained Inverse Problems with Graph Networks
- Rethinking Graph Neural Networks for Anomaly Detection
- Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection