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