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

326 papers, page 2 of 7

51. ICLR 2026Rapid Training of Hamiltonian Graph Networks Using Random Features
52. ICLR 2026Relational Graph Transformer
53. ICLR 2026Relatron: Automating Relational Machine Learning over Relational Databases
54. ICLR 2026Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
55. ICLR 2026Temporal Graph Thumbnail: Robust Representation Learning with Global Evolutionary Skeleton
56. ICLR 2026TetraGT: Tetrahedral Geometry-Driven Explicit Token Interactions with Graph Transformer for Molecular Representation Learning
57. ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
58. ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
59. ICML 2026A Unifying Relational Perspective on Expressive Lottery Tickets
60. ICML 2026Adaptive Memory Retention in Dynamic Graphs
61. ICML 2026Adaptive Node Feature Selection for Graph Neural Networks
62. ICML 2026Adaptive Recurrent Message Passing for Test Time Computing on Graphs
63. ICML 2026Adversarial Attacks and Robust Training for Hypergraph Neural Networks
64. ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
65. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
66. ICML 2026An Approximation Algorithm for Graph Label Selection
67. ICML 2026Anchor-guided Hypergraph Condensation with Dual-level Discrimination
68. ICML 2026Any-dimensional invariant universality
69. ICML 2026Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach
70. ICML 2026Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
71. ICML 2026Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
72. ICML 2026Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
73. ICML 2026Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
74. ICML 2026Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
75. ICML 2026Bias-Spectrum Neural Processes for Parametric PDEs: Architecture Priors Meet PDE Constraints
76. ICML 2026Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
77. ICML 2026BOCLOAK: Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
78. ICML 2026Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
79. ICML 2026Brain Networks Should Be Learned, Not Constructed
80. ICML 2026Bridging Structure and Semantics: Uncertainty-Modulated Dual-Path Diffusion for Robust Text-Attributed Graph Learning
81. ICML 2026CAffNet: Hard Constraint-Affine Neural Networks
82. ICML 2026CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures
83. ICML 2026Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
84. ICML 2026Capacity-Agnostic Parameter Isolation for Continual Graph Learning
85. ICML 2026CCLRec: Consensus-driven Contrastive Learning for LLM-enhanced Graph Recommendation
86. ICML 2026CELL: A Causal Perspective for Fairness-aware Graph Adaptation
87. ICML 2026CLINIC: Towards High-quality Graph Out-Of-Distribution Detection
88. ICML 2026Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
89. ICML 2026CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
90. ICML 2026D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
91. ICML 2026Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
92. ICML 2026DiP-G: Discrete Prompting for Graph Neural Networks
93. ICML 2026Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
94. ICML 2026Discriminative Attribute Graph Clustering Through Topology-Guided Contrastive Learning
95. ICML 2026Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
96. ICML 2026DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
97. ICML 2026Dual-channel Dynamic Graph Neural Networks with Adaptive Adjacency Learning and Multi-scale Representation Fusion
98. ICML 2026E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory
99. ICML 2026Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
100. ICML 2026Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch

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.