Deep learning · graph neural networks papers, 2023–2026

Key findings

• Deep learning · graph neural networks fell from 728 papers in 2023 to 326 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · graph neural networks work: 842 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 9 in 2023 to 20 in 2026.
• Among the 11 topics under Deep Learning, it ranks #5 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

All Deep learning · graph neural networks papers

1,648 papers, page 16 of 33

751. ICML 2024Simulation of Graph Algorithms with Looped Transformers
752. ICML 2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
753. ICML 2024Structure Your Data: Towards Semantic Graph Counterfactuals
754. ICML 2024Surprisingly Strong Performance Prediction with Neural Graph Features
755. ICML 2024The Expressive Power of Path-Based Graph Neural Networks
756. ICML 2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
757. ICML 2024Understanding Heterophily for Graph Neural Networks
758. ICML 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
759. ICML 2024Unsupervised Episode Generation for Graph Meta-learning
760. ICML 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
761. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
762. NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
763. NeurIPS 2024A Canonicalization Perspective on Invariant and Equivariant Learning
764. NeurIPS 2024A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
765. NeurIPS 2024A Foundation Model for Zero-shot Logical Query Reasoning
766. NeurIPS 2024A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking
767. NeurIPS 2024A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
768. NeurIPS 2024A Topology-aware Graph Coarsening Framework for Continual Graph Learning
769. NeurIPS 2024Accelerating Non-Maximum Suppression: A Graph Theory Perspective
770. NeurIPS 2024Almost Surely Asymptotically Constant Graph Neural Networks
771. NeurIPS 2024An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
772. NeurIPS 2024An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval
773. NeurIPS 2024Analysis of Corrected Graph Convolutions
774. NeurIPS 2024Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
775. NeurIPS 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learning
776. NeurIPS 2024Are Graph Neural Networks Optimal Approximation Algorithms?
777. NeurIPS 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
778. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
779. NeurIPS 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
780. NeurIPS 2024Boosting Graph Pooling with Persistent Homology
781. NeurIPS 2024Can Graph Learning Improve Planning in LLM-based Agents?
782. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
783. NeurIPS 2024Challenges of Generating Structurally Diverse Graphs
784. NeurIPS 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
785. NeurIPS 2024Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding
786. NeurIPS 2024Continuous Partitioning for Graph-Based Semi-Supervised Learning
787. NeurIPS 2024Continuous Product Graph Neural Networks
788. NeurIPS 2024DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
789. NeurIPS 2024Deep Equilibrium Algorithmic Reasoning
790. NeurIPS 2024Deep Graph Mating
791. NeurIPS 2024Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
792. NeurIPS 2024Deep Homomorphism Networks
793. NeurIPS 2024DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
794. NeurIPS 2024DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
795. NeurIPS 2024DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
796. NeurIPS 2024Dissecting the Failure of Invariant Learning on Graphs
797. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
798. NeurIPS 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
799. NeurIPS 2024Dynamic Rescaling for Training GNNs
800. NeurIPS 2024Efficient Graph Matching for Correlated Stochastic Block Models

FAQ

How many Deep learning · graph neural networks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,648 in total: 728 in 2023, 297 in 2024, 297 in 2025, 326 in 2026.

How many Deep learning · graph neural networks papers are at NeurIPS 2026?

NeurIPS 2026 has 102 Deep learning · graph neural networks papers, versus 91 the year before.

How many Deep learning · graph neural networks papers are at ICLR 2026?

ICLR 2026 has 32 Deep learning · graph neural networks papers, versus 50 the year before.

How many Deep learning · graph neural networks papers are at ICML 2026?

ICML 2026 has 168 Deep learning · graph neural networks papers, versus 141 the year before.

How many Deep learning · graph neural networks papers are at CVPR 2026?

CVPR 2026 has 24 Deep learning · graph neural networks papers, versus 15 the year before.

Which conference publishes the most Deep learning · graph neural networks papers?

NeurIPS, with 842 papers from 2023 to 2026.