NeurIPS 2025 Past Graphs
New Perspectives in Graph Machine Learning
NPGML
- Submission deadline
- Sep 8, 2025, 23:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (93)
Fetched from OpenReview (v2) on 2026-06-10.
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A Generative Framework for Exchangeable Graphs with Global and Local Latent Structure
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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models
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A New Perspective for Graph Learning Architecture Design: Linearize Your Depth Away
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A scalable platform to build the data layer of knowledge graph AI
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Actions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inference
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AI-Generated Text Detection using ISGraphs and Graph Neural Networks
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Are Large Language Models Good Temporal Graph Learners?
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Beyond Sparse Benchmarks: Evaluating GNNs with Realistic Missing Features
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Biological Pathway Informed Models with Graph Attention Networks (GAT)
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Bridging the Divide: End-to-End Sequence–Graph Learning
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Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
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Connected Causal Graphs for Real-World Science
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CR-Graphormer: From Cascades to Tokens via Mesoscopic Graph Rewiring
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CrediBench : Building Web-Scale Network Datasets for Information Integrity
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DAG Convolutional Networks
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Deep Modularity Networks with Diversity-Preserving Regularization
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Diffusion-augmented Graph Contrastive Learning for Collaborative Filtering
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Diffusion-Generated Social Graphs Enhance Bot Detection
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Discovering Transformer Circuits via a Hybrid Attribution and Pruning Framework
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Efficient and Expressive Graph Neural Networks
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Efficient Learning on Large Graphs using a Densifying Regularity Lemma
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EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks
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Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets
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Exploiting All Laplacian Eigenvectors for Node Classification with Graph Transformers
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Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics
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Exploring Heterophily in Graph-level Tasks
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Federated Link Prediction on Dynamic Graphs
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FedGraph: A Research Library and Benchmark for Federated Graph Learning
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FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
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Foundations for Robust yet Simple Sparse Hierarchical Pooling: A New Perspective on Sparse Graph Pooling
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G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
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Galois Theory Challenges Weisfeiler Leman: Invariant Features for Symmetric Matrices and Point Clouds
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GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
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Generalizable Insights for Graph Transformers in Theory and Practice
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Generating Directed Graphs with Dual Attention and Asymmetric Encoding
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GNN-Parametrized Diffusion Policies for Wireless Resource Allocation
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GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
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Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation
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Graph Neural Differential Equations in the Infinite‑Node Limit: Convergence and Rates via Graphon Theory
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Graph Representation Learning with Diffusion Generative Models
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Graph Semi-Supervised Learning for Point Classification on Data Manifolds
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Gromov-Wasserstein Graph Coarsening
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Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs
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HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
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Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees
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Inductive Transfer Learning for Graph-Based Recommenders
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Interpretable Regime Trajectories via Generative Graph State-Space Models
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KAN-GCN: Combining Kolmogorov–Arnold Network with Graph Convolution Network for an Accurate Ice Sheet Emulator
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Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs
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Laplacian-Guided Denoising Graph Diffusion for Graph Learning with an Adaptive Prior
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Learning (Approximately) Equivariant Networks via Constrained Optimization
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Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection
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Learning the Neighborhood: Contrast-Free Self-Supervised Molecular Graph Pretraining
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LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
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LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks
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Long-Range Graph Wavelet Networks
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Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Forecasting
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MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
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Model Extraction Without Graphs Structure: How Homophily Drives Attack Effectiveness
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Multi-view Graph Condensation via Tensor Decomposition
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Nonlinear Laplacians Improve Signed-Directed Graph Learning
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Of Graphs and Tables: Zero-Shot Node Classification with Tabular Foundation Models
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On the (Non) Injectivity of Piecewise Linear Janossy Pooling
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Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
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Posterior Label Smoothing for Node Classification
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Predict Training Data Quality via Its Geometry in Metric Space
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Predicting Microbial Interactions Using Graph Neural Networks
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Rademacher Meets Colors: More Expressivity, but at What Cost?
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Re-evaluating the Advancements of Heterophilic Graph Learning
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RELATE: A Schema-Agnostic Cross-Attention Encoder for Multimodal Relational Graphs
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Rethinking Graph Backdoor Defense: A Topological, Coarse-to-Fine Perspective
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Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
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Second-Order Tensorial Partial Differential Equations on Graphs
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Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
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Semantic Priors for Drug–Drug Interaction Prediction Using Compact Graph Encoders
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Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs
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Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
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Staleness-based Subgraph Sampling for Training GNNs on Large-Scale Graphs
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Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization
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Temporal Graph AutoEncoder: Mapping Dynamic Graphs to Dynamical Systems with Neural ODEs
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The Cartesian Gaussian Additive Noise Model for Causal Inference with Dependent Samples
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The GNN as a Low-Pass Filter: A Spectral Perspective on Achieving Stability in Neural PDE Solvers
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Topological Clustering of Aphasic Brain Networks
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Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
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Transferability of Graph Transformers with Convolutional Positional Encodings
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Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps
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Turning Tabular Foundation Models into Graph Foundation Models
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Uncertainty-Aware Message Passing Neural Networks
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Understanding Generalization in Node and Link Prediction
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Unrolled Policy Iteration Via Graph Filters
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Wasserstein Hypergraph Neural Network
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When Curvature Beats Dimension: Euclidean Limits and Hyperbolic Design Rules for Trees
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WindMiL: Equivariant Graph Learning for Wind Loading Prediction