This repo contains a comprehensive compilation of graph and/or GNN papers that were accepted at the Thirty-Eighth Annual Conference on Neural Information Processing Systems 2024. Graph or Geometric machine learning possesses an indispensable role within the domain of machine learning research, providing invaluable insights, methodologies, and solutions to a diverse array of challenges and problems.
Short Overview: We've got around 450-500 papers focusing on Graphs and GNNs in NeurIPS'24, almost 1.75-2x of ICML'24. Seems like diffusion, transformers, agents and knowledge graphs had a good focus in NeurIPS'24.
Have a look and throw me a review (and, a star ⭐, maybe!) Thanks!
View Topic list!
- GNN Theories
- Weisfeiler Leman
- Heterophily
- Hypergraph
- Expressivity
- Generalization
- Equivariant Graph Neural Networks
- Out-of-Distribution
- Diffusion
- Graph Matching
- Flow Matching
- Contrastive Learning
- Clustering
- Foundational Models
- Message Passing Neural Networks
- Transformers
- Optimal Transport
- Graph Generation
- Unsupervised Learning
- Meta-learning
- Disentanglement
- Others
- GNNs for PDE/ODE/Physics
- Graph and Large Language Models/Agents
- Knowledge Graph and Knowledge Graph Embeddings
- GNN Applications
- Spatial and/or Temporal GNNs
- Explainable AI
- Reinforcement Learning
- Graphs, Molecules and Biology
- GFlowNets
- Causal Discovery and Graphs
- Federated Learning, Privacy, Decentralization
- Scene Graphs
- Graphs, GNNs and Efficiency
- Others
- More Possible Works
- Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles
- On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
- Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover
- Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph
- Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
- Assembly Fuzzy Representation on Hypergraph for Open-Set 3D Object Retrieval
- On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks
- On the Expressive Power of Tree-Structured Probabilistic Circuits
- Bridging OOD Detection and Generalization: A Graph-Theoretic View
- Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond
- Improving Generalization of Dynamic Graph Learning via Environment Prompt
- Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
- Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
- A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
- Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
- Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
- Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
- Equivariant spatio-hemispherical networks for diffusion MRI deconvolution
- ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
- Approximately Equivariant Neural Processes
- Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
- Equivariant Neural Diffusion for Molecule Generation
- Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity
- Revisiting Score Propagation in Graph Out-of-Distribution Detection
- PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling
- Efficient Graph Matching for Correlated Stochastic Block Models
- Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval
- FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
- Fisher Flow Matching for Generative Modeling over Discrete Data
- Variational Flow Matching for Graph Generation
- Generalized Protein Pocket Generation with Prior-Informed Flow Matching
- Embedding Dimension of Contrastive Learning and $k$-Nearest Neighbors
- A probability contrastive learning framework for 3D molecular representation learning
- How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
- Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
- Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
- FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
- Unified Graph Augmentations for Generalized Contrastive Learning on Graphs
- Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
- Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
- Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
- MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs
- Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
- SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning
- DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
- Diffusion Twigs with Loop Guidance for Conditional Graph Generation
- Graph Diffusion Transformers for Multi-Conditional Molecular Generation
- NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
- Differentially Private Graph Diffusion with Applications in Personalized PageRanks
- Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
- Discrete-state Continuous-time Diffusion for Graph Generation
- Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
- Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
- Geometric Trajectory Diffusion Models
- DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
- DiffuBox: Refining 3D Object Detection with Point Diffusion
- Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
- Graph Diffusion Policy Optimization
- Faster Local Solvers for Graph Diffusion Equations
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement
- HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
- Graph Neural Networks Need Cluster-Normalize-Activate Modules
- DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
- Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding
- Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
- TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering
- How Does Message Passing Improve Collaborative Filtering?
- Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
- Pure Message Passing Can Estimate Common Neighbor for Link Prediction
- Towards Dynamic Message Passing on Graphs
- Cell ontology guided transcriptome foundation model
- A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
- A Foundation Model for Zero-shot Logical Query Reasoning
- MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
- GFT: Graph Foundation Model with Transferable Tree Vocabulary
- Supra-Laplacian Encoding for Transformer on Dynamic Graphs
- Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
- Long-range Brain Graph Transformer
- Graph Convolutions Enrich the Self-Attention in Transformers!
- Molecule Design by Latent Prompt Transformer
- EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
- Graph Diffusion Transformers for Multi-Conditional Molecular Generation
- CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
- Knowledge Circuits in Pretrained Transformers
- ProTransformer: Robustify Transformers via Plug-and-Play Paradigm
- Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
- Towards Principled Graph Transformers
- Even Sparser Graph Transformers
- Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
- $\textit{NeuroPath}$: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
- Understanding Transformer Reasoning Capabilities via Graph Algorithms
- Transformers need glasses! Information over-squashing in language tasks
- Finding Transformer Circuits With Edge Pruning
- ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
- Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
- Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
- Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling
- Fairness in Social Influence Maximization via Optimal Transport
- Scene Graph Generation with Role-Playing Large Language Models
- Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
- Diffusion Twigs with Loop Guidance for Conditional Graph Generation
- Discrete-state Continuous-time Diffusion for Graph Generation
- FairWire: Fair Graph Generation
- Adaptive Visual Scene Understanding: Incremental Scene Graph Generation
- Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization
- Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
- Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models
- DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
- Long-range Meta-path Search on Large-scale Heterogeneous Graphs
- What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
- Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
- Online Relational Inference for Evolving Multi-agent Interacting Systems
- Can Graph Learning Improve Planning in LLM-based Agents?
- UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction
- Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
- Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context
- GraphVis: Boosting LLMs with Visual Knowledge Graph Integration
- Ad Auctions for LLMs via Retrieval Augmented Generation
- Transcoders find interpretable LLM feature circuits
- SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation
- LLM Dataset Inference: Did you train on my dataset?
- LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
- KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
- GraphVis: Boosting LLMs with Visual Knowledge Graph Integration
- Knowledge Graph Completion by Intermediate Variables Regularization
- A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
- DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
- Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
- KnowGPT: Knowledge Graph based Prompting for Large Language Models
- UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction
- Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
- Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
- Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding
- Learning from Highly Sparse Spatio-temporal Data
- EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
- A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking
- DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
- State Space Models on Temporal Graphs: A First-Principles Study
- Improving Temporal Link Prediction via Temporal Walk Matrix Projection
- Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
- Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
- Differentially Private Graph Diffusion with Applications in Personalized PageRanks
- A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds
- PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
- Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
- A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis
- HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
- Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series
- Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning
- A hierarchical decomposition for explaining ML performance discrepancies
- RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
- MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs
- Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
- GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
- Transcoders find interpretable LLM feature circuits
- Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning
- FlexPlanner: Flexible 3D Floorplanning via Deep Reinforcement Learning in Hybrid Action Space with Multi-Modality Representation
- Optimizing Automatic Differentiation with Deep Reinforcement Learning
- On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
- Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
- Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
- Enhancing Chess Reinforcement Learning with Graph Representation
- Amortized Active Causal Induction with Deep Reinforcement Learning
- Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
- TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
- Conditional Synthesis of 3D Molecules with Time Correction Sampler
- Molecule Design by Latent Prompt Transformer
- UniIF: Unified Molecule Inverse Folding
- QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation
- Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
- Score-based 3D molecule generation with neural fields
- Molecule Generation with Fragment Retrieval Augmentation
- FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
- ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
- Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure
- RGFN: Synthesizable Molecular Generation Using GFlowNets
- Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
- Causal Discovery from Event Sequences by Local Cause-Effect Attribution
- Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
- Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
- Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
- Disentangled Representation Learning in Non-Markovian Causal Systems
- On Causal Discovery in the Presence of Deterministic Relations
- Learning the Latent Causal Structure for Modeling Label Noise
- A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
- Interventional Causal Discovery in a Mixture of DAGs
- Identifying General Mechanism Shifts in Linear Causal Representations
- Causal Effect Identification in a Sub-Population with Latent Variables
- Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
- Causal discovery with endogenous context variables
- On the Complexity of Identification in Linear Structural Causal Models
- Learning Mixtures of Unknown Causal Interventions
- Sample Complexity of Interventional Causal Representation Learning
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions
- Linear Causal Bandits: Unknown Graph and Soft Interventions
- Identifying Causal Effects Under Functional Dependencies
- A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
- Consistency of Neural Causal Partial Identification
- On the Parameter Identifiability of Partially Observed Linear Causal Models
- QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
- Amortized Active Causal Induction with Deep Reinforcement Learning
- Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
- CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
- FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
- FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
- Federated Graph Learning for Cross-Domain Recommendation
- FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
- Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
- On provable privacy vulnerabilities of graph representations
- Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
- Scene Graph Generation with Role-Playing Large Language Models
- SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation
- Adaptive Visual Scene Understanding: Incremental Scene Graph Generation
- Multiview Scene Graph
- Cost-efficient Knowledge-based Question Answering with Large Language Models
- An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions
- Efficient Policy Evaluation Across Multiple Different Experimental Datasets
- Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
- Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
- Efficient Streaming Algorithms for Graphlet Sampling
- Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
- Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
- Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
- ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
- Gaussian Graph Network: Learning Efficient and Generalizable Gaussian Representations from Multi-view Images
- GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
- Graph Edit Distance with General Costs Using Neural Set Divergence
- What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
- Generative Modelling of Structurally Constrained Graphs
- Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
- Boosting Graph Pooling with Persistent Homology
- GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting
- ARC: A Generalist Graph Anomaly Detector with In-Context Learning
- Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images
- Road Network Representation Learning with the Third Law of Geography
- Bayesian Optimization of Functions over Node Subsets in Graphs
- IF-Font: Ideographic Description Sequence-Following Font Generation
- Almost Surely Asymptotically Constant Graph Neural Networks
- Distributed-Order Fractional Graph Operating Network
- DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
- Generalizing CNNs to graphs with learnable neighborhood quantization
- InstructG2I: Synthesizing Images from Multimodal Attributed Graphs
- On the Scalability of GNNs for Molecular Graphs
- Spiking Graph Neural Network on Riemannian Manifolds
- Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
- DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ
- Idiographic Personality Gaussian Process for Psychological Assessment
- What do Graph Neural Networks learn? Insights from Tropical Geometry
- Accelerating Non-Maximum Suppression: A Graph Theory Perspective
- Cryptographic Hardness of Score Estimation
- Graph Neural Networks and Arithmetic Circuits
- Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
- Probabilistic Graph Rewiring via Virtual Nodes
- Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
- SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision
- Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos
- Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
- Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
- UniGAD: Unifying Multi-level Graph Anomaly Detection
- GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
- Visual Data Diagnosis and Debiasing with Concept Graphs
- Deep Graph Mating
- Energy-based Epistemic Uncertainty for Graph Neural Networks
- Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion
- Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
- Similarity-Navigated Conformal Prediction for Graph Neural Networks
- Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits
- Graph Classification via Reference Distribution Learning: Theory and Practice
- Continuous Product Graph Neural Networks
- Uncovering the Redundancy in Graph Self-supervised Learning Models
- Logical characterizations of recurrent graph neural networks with reals and floats
- Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
- DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
- DistrictNet: Decision-aware learning for geographical districting
- Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
- Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
- Unitary Convolutions for Learning on Graphs and Groups
- GRANOLA: Adaptive Normalization for Graph Neural Networks
- Mind the Graph When Balancing Data for Fairness or Robustness
- Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
- Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
- Analysis of Corrected Graph Convolutions
- GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
- Microstructures and Accuracy of Graph Recall by Large Language Models
- Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
- Continuous Partitioning for Graph-Based Semi-Supervised Learning
- On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
- What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
- Gradient Rewiring for Editable Graph Neural Network Training
- Graph Learning for Numeric Planning
- Towards Harmless Rawlsian Fairness Regardless of Demographic Prior
- DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
- Dissecting the Failure of Invariant Learning on Graphs
- Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
- Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
- Spatio-Spectral Graph Neural Networks
- Motion Graph Unleashed: A Novel Approach to Video Prediction
- Bridge the Points: Graph-based Few-shot Segment Anything Semantically
- Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
- RAGraph: A General Retrieval-Augmented Graph Learning Framework
- Learning on Large Graphs using Intersecting Communities
- DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph
- UGC: Universal Graph Coarsening
- Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
- LLaMo: Large Language Model-based Molecular Graph Assistant
- Are Graph Neural Networks Optimal Approximation Algorithms?
- Spectral Graph Pruning Against Over-Squashing and Over-Smoothing
- The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
- Graph Coarsening with Message-Passing Guarantees
- CSPG: Crossing Sparse Proximity Graphs for Approximate Nearest Neighbor Search
- Dynamic Rescaling for Training GNNs
- An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval
- EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics
- Robust Offline Active Learning on Graphs
- Active design of two-photon holographic stimulation for identifying neural population dynamics
- Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs
- G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
- The Intelligible and Effective Graph Neural Additive Network
- DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
- A Structure-Aware Framework for Learning Device Placements on Computation Graphs
- Fairness-Aware Estimation of Graphical Models
- HGDL: Heterogeneous Graph Label Distribution Learning
- A Topology-aware Graph Coarsening Framework for Continual Graph Learning
- Robust Graph Neural Networks via Unbiased Aggregation
- Challenges of Generating Structurally Diverse Graphs
- Mixture of Link Predictors on Graphs
- Regression under demographic parity constraints via unlabeled post-processing
- Graph neural networks and non-commuting operators
- Graph-based Uncertainty Metrics for Long-form Language Model Generations
- Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
- Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation
- Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
- GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
- FUGAL: Feature-fortified Unrestricted Graph Alignment
- Graphcode: Learning from multiparameter persistent homology using graph neural networks
- Stochastic contextual bandits with graph feedback: from independence number to MAS number
- Generative Semi-supervised Graph Anomaly Detection
- GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs
- Non-convolutional graph neural networks.
- Linear Uncertainty Quantification of Graphical Model Inference
- Graph Neural Networks Do Not Always Oversmooth
- Schur Nets: exploiting local structure for equivariance in higher order graph neural networks
(Needs Verification Yet)
- Markov Equivalence and Consistency in Differentiable Structure Learning
- SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models
- On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models
- Domain Adaptation for Large-Vocabulary Object Detectors
- Tackling Uncertain Correspondences for Multi-Modal Entity Alignment
- Learning rigid-body simulators over implicit shapes for large-scale scenes and vision
- Combining Observational Data and Language for Species Range Estimation
- GS-Hider: Hiding Messages into 3D Gaussian Splatting
- Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
- Instance-Optimal Private Density Estimation in the Wasserstein Distance
- DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering
- Evaluating the World Model Implicit in a Generative Model
- Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
- Fair Wasserstein Coresets
- Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
- Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
- DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation
- Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation
- EGonc : Energy-based Open-Set Node Classification with substitute Unknowns
- Practical Shuffle Coding
- Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell RNA-Seq Data Imputation
- Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
- Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting
- Divergences between Language Models and Human Brains
- Expected Probabilistic Hierarchies
- If You Want to Be Robust, Be Wary of Initialization
- Accelerating ERM for data-driven algorithm design using output-sensitive techniques
- Towards Flexible Visual Relationship Segmentation
- MKGL: Mastery of a Three-Word Language
- GSDF: 3DGS Meets SDF for Improved Neural Rendering and Reconstruction
- Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials
- Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
- Non-Euclidean Mixture Model for Social Network Embedding
- EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals
- PageRank Bandits for Link Prediction
- Deep Homomorphism Networks
- HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
- Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures
- Sequential Harmful Shift Detection Without Labels
- Dynamic 3D Gaussian Fields for Urban Areas
- Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
- Amortized Eigendecomposition for Neural Networks
- Transferable Boltzmann Generators
- Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation
- UniAR: A Unified model for predicting human Attention and Responses on visual content
- Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
- Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
- Deep Equilibrium Algorithmic Reasoning
- Semantic Routing via Autoregressive Modeling
- Learning Representations for Hierarchies with Minimal Support
- From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos
- DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection
- Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections
- Hardness of Learning Neural Networks under the Manifold Hypothesis
- TopoFR: A Closer Look at Topology Alignment on Face Recognition
- IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
- Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
- UniMTS: Unified Pre-training for Motion Time Series
- HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
- eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling
- Breaking the curse of dimensionality in structured density estimation
- Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
- GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
- The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
- Synergistic Dual Spatial-aware Generation of Image-to-text and Text-to-image
- Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective
- On Differentially Private U Statistics
- Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
- Generative Hierarchical Materials Search
- ChatCam: Empowering Camera Control through Conversational AI
- Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
- Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling
- Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals
- Estimating Epistemic and Aleatoric Uncertainty with a Single Model
- NeuralSteiner: Learning Steiner Tree for Overflow-avoiding Global Routing in Chip Design
- Consensus Learning with Deep Sets for Essential Matrix Estimation
- Towards Effective Planning Strategies for Dynamic Opinion Networks
- Learning Low-Rank Feature for Thorax Disease Classification
- Smoothie: Label Free Language Model Routing
- SpeAr: A Spectral Approach for Zero-Shot Node Classification
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization
- Metric Space Magnitude for Evaluating the Diversity of Latent Representations
- The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
- Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations
- Transfer Learning for Latent Variable Network Models
- Testing Calibration in Nearly-Linear Time
- Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
- Relational Concept Bottleneck Models
- Navigating Chemical Space with Latent Flows
- Post-Hoc Reversal: Are We Selecting Models Prematurely?
- Geodesic Optimization for Predictive Shift Adaptation on EEG data
- Neural Pfaffians: Solving Many Many-Electron Schrodinger Equations
- Differentiable Structure Learning with Partial Orders
- Taming the Long Tail in Human Mobility Prediction
- CLIP in Mirror: Disentangling text from visual images through reflection
- Multilingual Diversity Improves Vision-Language Representations
- Expert-level protocol translation for self-driving labs
- G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models
- Learning Discrete Concepts in Latent Hierarchical Models
- MeMo: Meaningful, Modular Controllers via Noise Injection
- Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
- Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections
- Generative Forests
- bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction
- Inversion-based Latent Bayesian Optimization
- ST$_k$: A Scalable Module for Solving Top-k Problems
- DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering
- Counterfactual Fairness by Combining Factual and Counterfactual Predictions
- AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
- Iterative Methods via Locally Evolving Set Process
- Can Models Learn Skill Composition from Examples?
- Exponential Quantum Communication Advantage in Distributed Inference and Learning
- DeiSAM: Segment Anything with Deictic Prompting
- Group Robust Preference Optimization in Reward-free RLHF
- Harnessing Multiple Correlated Networks for Exact Community Recovery
- Why the Metric Backbone Preserves Community Structure
- MambaTree: Tree Topology is All You Need in State Space Model
- On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization
- Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
- Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
- Information Re-Organization Improves Reasoning in Large Language Models
- Qualitative Mechanism Independence
- Persistent Homology for High-dimensional Data Based on Spectral Methods
- Fairness without Harm: An Influence-Guided Active Sampling Approach
- Extracting Training Data from Molecular Pre-trained Models
- Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
- LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes
- On the Computational Landscape of Replicable Learning
- What type of inference is planning?
- Shape analysis for time series
- realSEUDO for real-time calcium imaging analysis
- Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
- DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
- Exploring Molecular Pretraining Model at Scale
- FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing
- Entity Alignment with Noisy Annotations from Large Language Models
- End-to-End Ontology Learning with Large Language Models
- Questioning the Survey Responses of Large Language Models
- Mixture of neural fields for heterogeneous reconstruction in cryo-EM
- TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing
- SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network
- Latent Intrinsics Emerge from Training to Relight
- Fractal Patterns May Illuminate the Success of Next-Token Prediction
- Large language model validity via enhanced conformal prediction methods
- Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
Missing any paper? If any paper is absent from the list, please feel free to mail or open an issue or submit a pull request. I'll gladly add that! Also, If I mis-categorized, please knock!
- Awesome NeurIPS'24 Molecular ML Paper Collection
- Awesome ICML 2024 Graph Paper Collection
- Awesome ICLR 2024 Graph Paper Collection
- Awesome-LLMs-ICLR-24
Azmine Toushik Wasi
