Diffusion

• Generalization Error of Graph Neural Networks in the Mean-field Regime
• Graph Adversarial Diffusion Convolution
• Editing Partially Observable Networks via Graph Diffusion Models
• Cluster-Aware Similarity Diffusion for Instance Retrieval
• Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
• On dimensionality of feature vectors in MPNNs
• Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation
• Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation
• Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
• Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
• Pairwise Alignment Improves Graph Domain Adaptation
• From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
• Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
• Hyperbolic Geometric Latent Diffusion Model for Graph Generation
• Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
• LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
• A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
• Verifying message-passing neural networks via topology-based bounds tightening
• PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
• Pluvial Flood Emulation with Hydraulics-informed Message Passing
• Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
• Class-Imbalanced Graph Learning without Class Rebalancing
• Automated Loss function Search for Class-imbalanced Node Classification
• Graph Generation with Diffusion Mixture
• Learning Iterative Reasoning through Energy Diffusion
• Multi-View Clustering by Inter-cluster Connectivity Guided Reward
• Dynamic Spectral Clustering with Provable Approximation Guarantee
• EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization
• Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
• Graph Distillation with Eigenbasis Matching
• Graph Neural Networks Use Graphs When They Shouldn't
• Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet
• GNNs Also Deserve Editing, and They Need It More Than Once
• Learning Divergence Fields for Shift-Robust Graph Representations
• Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
• Efficient Contextual Bandits with Uninformed Feedback Graphs
• Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
• What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
• Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
• Less is More: on the Over-Globalizing Problem in Graph Transformers
• Comparing Graph Transformers via Positional Encodings
• Generalized Sobolev Transport for Probability Measures on a Graph
• On the Role of Edge Dependency in Graph Generative Models
• Unsupervised Episode Generation for Graph Meta-learning
• Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity
• Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
• Efficient Contrastive Learning for Fast and Accurate Inference on Graphs
• Quantum Positional Encodings for Graph Neural Networks
• SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
• How Interpretable Are Interpretable Graph Neural Networks?
• Graph Neural Networks with a Distribution of Parametrized Graphs
• Cooperative Graph Neural Networks
• Graph Geometry-Preserving Autoencoders
• Stereographic Spherical Sliced Wasserstein Distances
• Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
• Prospector Heads: Generalized Feature Attribution for Large Models & Data
• Collective Certified Robustness against Graph Injection Attacks
• Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
• Homomorphism Counts for Graph Neural Networks: All About That Basis
• HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
• Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs
• SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
• Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
• EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
• How Graph Neural Networks Learn: Lessons from Training Dynamics
• Simulation of Graph Algorithms with Looped Transformers
• Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks
• Gaussian Processes on Cellular Complexes
• Recurrent Distance Filtering for Graph Representation Learning
• Graph External Attention Enhanced Transformer
• A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• Convergence Guarantees for the DeepWalk Embedding on Block Models
• An Efficient Maximal Ancestral Graph Listing Algorithm
• Extending Test-Time Augmentation with Metamorphic Relations for Combinatorial Problems
• Graph Positional and Structural Encoder
• Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
• Exploring Correlations of Self-Supervised Tasks for Graphs
• Surprisingly Strong Performance Prediction with Neural Graph Features
• Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS
• Uncertainty for Active Learning on Graphs
• Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
• Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
• Perfect Alignment May be Poisonous to Graph Contrastive Learning
• Aligning Transformers with Weisfeiler-Leman

• Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
• Graph Out-of-Distribution Detection Goes Neighborhood Shaping
• Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
• Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
• Robust Graph Matching when Nodes are Corrupt
• Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
• On the Generalization of Equivariant Graph Neural Networks
• Semantically-correlated memories in a dense associative model
• Community-Invariant Graph Contrastive Learning
• Graph Structure Extrapolation for Out-of-Distribution Generalization
• When and How Does In-Distribution Label Help Out-of-Distribution Detection?
• Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
• Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
• Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning
• Hypergraph-enhanced Dual Semi-supervised Graph Classification
• Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
• On the Expressive Power of Spectral Invariant Graph Neural Networks
• The Expressive Power of Path-Based Graph Neural Networks
• Weisfeiler-Leman at the margin: When more expressivity matters
• Weisfeiler Leman for Euclidean Equivariant Machine Learning
• Understanding Heterophily for Graph Neural Networks
• How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
• Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
• Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing
• Topological Neural Networks go Persistent, Equivariant, and Continuous
• Interpreting Equivariant Representations
• Graph Automorphism Group Equivariant Neural Networks
• An Empirical Study of Realized GNN Expressiveness

Diffusion

• Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
• Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization
• Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
• A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
• OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport
• LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits
• SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
• Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
• CARTE: Pretraining and Transfer for Tabular Learning
• Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
• Graph2Tac: Online Representation Learning of Formal Math Concepts
• The Merit of River Network Topology for Neural Flood Forecasting
• Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds

Diffusion

• Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
• Equivariant Graph Neural Operator for Modeling 3D Dynamics
• Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
• Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
• PGODE: Towards High-quality System Dynamics Modeling
• HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
• Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
• PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
• Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better

Diffusion

• Gaussian Plane-Wave Neural Operator for Electron Density Estimation
• Expressivity and Generalization: Fragment-Biases for Molecular GNNs
• UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
• CHEMREASONER: Heuristic Search over a Large Language Models Knowledge Space using Quantum-Chemical Feedback
• Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
• Representing Molecules as Random Walks Over Interpretable Grammars
• Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
• Modelling Microbial Communities with Graph Neural Networks
• Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
• Projecting Molecules into Synthesizable Chemical Spaces
• Proteus: Exploring Protein Structure Generation for Enhanced Designability and Efficiency

Diffusion

• Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
• GFlowNet Training by Policy Gradients
• Embarrassingly Parallel GFlowNets
• Learning to Scale Logits for Temperature-Conditional GFlowNets

Diffusion

• S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training
• Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
• Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
• Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
• Long Range Propagation on Continuous-Time Dynamic Graphs

Diffusion

• Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
• Structure Your Data: Towards Semantic Graph Counterfactuals
• Explaining Graph Neural Networks via Structure-aware Interaction Index
• Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
• EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
• Graph Neural Network Explanations are Fragile

Diffusion

• PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
• Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
• Knowledge Graphs Can be Learned with Just Intersection Features
• KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning
• Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
• Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
• Knowledge-aware Reinforced Language Models for Protein Directed Evolution

Diffusion

• Position Paper: Future Directions in the Theory of Graph Machine Learning
• Position: Graph Foundation Models Are Already Here
• SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code
• Differentiability and Optimization of Multiparameter Persistent Homology
• Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition
• Position: Topological Deep Learning is the New Frontier for Relational Learning
• Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
• Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
• Open Ad Hoc Teamwork with Cooperative Game Theory
• Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach
• Predicting Lagrangian Multipliers for Mixed Integer Linear Programs
• MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
• Dynamic Metric Embedding into lp Space
• Graph Mixup on Approximate GromovWasserstein Geodesics
• Graph-Triggered Rising Bandits
• On Interpolating Experts and Multi-Armed Bandits
• Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
• Empowering Graph Invariance Learning with Deep Spurious Infomax
• Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
• Predictive Coding beyond Correlations
• When is Transfer Learning Possible?
• Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
• Graph As Point Set
• CKGConv: General Graph Convolution with Continuous Kernels
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
• VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
• Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
• Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
• DUPLEX: Dual GAT for Complex Embedding of Directed Graphs
• MS-TIP: Imputation Aware Pedestrian Trajectory Prediction
• Learning Graph Representation via Graph Entropy Maximization
• QBMK: Quantum-based Matching Kernels for Un-attributed Graphs
• Mitigating Label Noise on Graphs via Topological Sample Selection
• Multi-View Stochastic Block Models
• Learning in Deep Factor Graphs with Gaussian Belief Propagation
• Individual Fairness in Graph Decomposition
• Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
• Sign Rank Limitations for Inner Product Graph Decoders
• Differentiable Mapper for Topological Optimization of Data Representation
• Incremental Topological Ordering and Cycle Detection with Predictions
• Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency

Diffusion

• Optimal Transport for Structure Learning Under Missing Data
• Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
• Causal Effect Identification in LiNGAM Models with Latent Confounders
• Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
• Causal Representation Learning from Multiple Distributions: A General Setting
• Foundations of Testing for Finite-Sample Causal Discovery
• Optimal Kernel Choice for Score Function-based Causal Discovery
• Causal Discovery with Fewer Conditional Independence Tests
• How Transformers Learn Causal Structure with Gradient Descent
• From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks
• A Fixed-Point Approach for Causal Generative Modeling
• Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks
• Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
• Discovering Mixtures of Structural Causal Models from Time Series Data
• Adaptive Online Experimental Design for Causal Discovery
• Stable Differentiable Causal Discovery

Diffusion

• GPTSwarm: Language Agents as Optimizable Graphs
• Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
• Case-Based or Rule-Based: How Do Transformers Do the Math?
• LLaGA: Large Language and Graph Assistant
• MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

Diffusion

• Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
• SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
• HGAP: Boosting Permutation Invariant and Permutation Equivariant in Multi-Agent Reinforcement Learning via Graph Attention Network
• Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
• Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments

Diffusion

• Federated Self-Explaining GNNs with Anti-shortcut Augmentations
• The Privacy Power of Correlated Noise in Decentralized Learning
• Privacy Attacks in Decentralized Learning
• Differentially Private Decentralized Learning with Random Walks
• Effective Federated Graph Matching
• Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients

Diffusion

• Awesome **NeurIPS'24** ***Molecular ML*** Paper Collection
• **Awesome NeurIPS 2024 Graph Paper Collection**
• **Awesome ICLR 2024 Graph Paper Collection**