NeurIPS 2026 accepted papers on Graph learning
175 of the 6,080 papers accepted at NeurIPS 2026 (2.9%) are on Graph learning, compared with 3.5% at NeurIPS 2025. They fall into 4 topics.
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Topics and papers
Graph self-supervised learning · 63 papers
- A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
- A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
- AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding
- Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
- Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning
- DiPhon: Diffusion on Graphons for Scalable Graph Generation
- DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
- Flowette: Flow Matching with Graphette Priors for Graph Generation
- Higher-Order Cell Tracking Transformer
- Improving LLM Final Representations with Inter-Layer Geometry
- Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis
- Information Loss and Disparate Effects in Network Embeddings
and 51 more in the interactive map.
GNN architectures · 59 papers
- Equivariant Spherical Transformer for Efficient Molecular Modeling (1 citation)
- B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
- Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
- Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
- Expressive Power of Deep Homomorphism Networks over Relational Databases
- Function graph transformers universally approximate operators between function spaces
- Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
- GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
- GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
- Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
- GraphIP–Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
- GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
and 47 more in the interactive map.
Knowledge and temporal graphs · 29 papers
- A Closer Look at Dynamic Scene Graph Generation in the Era of Multimodal Large Language Models
- COMPOSE: Composing Future Theorems from Citations and Formal Structure
- EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge
- GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis
- Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
- Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
- NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
- Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning
- A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
- Differentiable Cluster Discovery in Temporal Graphs
- ExoC2T: An exogenous-driven spatio-temporal learning framework for cross-city transfer
- GRNAgent: A Multimodal Graph Reasoning Agent for Gene Regulatory Network Inference
and 17 more in the interactive map.
Graph algorithms and clustering · 24 papers
- Amortized Molecular Optimization via Group Relative Policy Optimization
- AT-SKM-Net: An Accelerated Trainable Sampling Kaczmarz-Motzkin Framework for Linear Hard-Constraint Feasibility on Dynamic Graphs
- Chaining 2-FWL GNNs for Combinatorial Graph Alignment
- Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
- Euclidean Embedding of Data Using Local Distances
- Generalized Priority-Aware Shapley Value
- MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search
- Query-Limited Community Recovery in Stochastic Block Models
- The Query Complexity of Local Search in Rounds on General Graphs
- Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition
- AlphaPROBE: Alpha Mining via principled retriveval and on-graph biased evolution
- Bridging Graph Worlds: Neural Approximation of Gromov-Wasserstein Distances
and 12 more in the interactive map.