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 14 of 33

651. ICLR 2024Graph Parsing Networks
652. ICLR 2024Graph Transformers on EHRs: Better Representation Improves Downstream Performance
653. ICLR 2024GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
654. ICLR 2024GraphPulse: Topological representations for temporal graph property prediction
655. ICLR 2024GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
656. ICLR 2024HiGen: Hierarchical Graph Generative Networks
657. ICLR 2024Hybrid Directional Graph Neural Network for Molecules
658. ICLR 2024iGraphMix: Input Graph Mixup Method for Node Classification
659. ICLR 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
660. ICLR 2024Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network
661. ICLR 2024Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model
662. ICLR 2024Local Graph Clustering with Noisy Labels
663. ICLR 2024Locality-Aware Graph Rewiring in GNNs
664. ICLR 2024MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
665. ICLR 2024Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
666. ICLR 2024One For All: Towards Training One Graph Model For All Classification Tasks
667. ICLR 2024Orbit-Equivariant Graph Neural Networks
668. ICLR 2024PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
669. ICLR 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
670. ICLR 2024PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
671. ICLR 2024Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
672. ICLR 2024Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
673. ICLR 2024Robust Angular Synchronization via Directed Graph Neural Networks
674. ICLR 2024SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
675. ICLR 2024StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
676. ICLR 2024TEDDY: Trimming Edges with Degree-based Discrimination Strategy
677. ICLR 2024Temporal Generalization Estimation in Evolving Graphs
678. ICLR 2024Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
679. ICLR 2024Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
680. ICLR 2024Training Graph Transformers via Curriculum-Enhanced Attention Distillation
681. ICLR 2024Uncertainty-aware Graph-based Hyperspectral Image Classification
682. ICLR 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
683. ICLR 2024Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
684. ICLR 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
685. ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
686. ICML 2024A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
687. ICML 2024An Efficient Maximal Ancestral Graph Listing Algorithm
688. ICML 2024CKGConv: General Graph Convolution with Continuous Kernels
689. ICML 2024Class-Imbalanced Graph Learning without Class Rebalancing
690. ICML 2024Collective Certified Robustness against Graph Injection Attacks
691. ICML 2024Community-Invariant Graph Contrastive Learning
692. ICML 2024Comparing Graph Transformers via Positional Encodings
693. ICML 2024Cooperative Graph Neural Networks
694. ICML 2024Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
695. ICML 2024Editing Partially Observable Networks via Graph Diffusion Models
696. ICML 2024Effective Federated Graph Matching
697. ICML 2024Empowering Graph Invariance Learning with Deep Spurious Infomax
698. ICML 2024Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
699. ICML 2024EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
700. ICML 2024Explaining Graph Neural Networks via Structure-aware Interaction Index

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.