Graph Neural Networks papers, 2023–2026

Key findings

• Graph Neural Networks fell from 434 papers in 2023 to 183 in 2026, so interest is cooling.
• NeurIPS publishes the most Graph Neural Networks work: 489 papers from 2023 to 2026, 47% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 10 in 2023 to 10 in 2026.
• Among the 5 topics under Graph and Structured Data, it ranks #4 by 2026 paper count; the fastest grower in the group, Time Series, is up 3.2x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
• ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
• ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
• ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
• ICLR 2025Edge Prompt Tuning for Graph Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
• CVPR 2024FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
• ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
• CVPR 2023Adversarially Robust Neural Architecture Search for Graph Neural Networks
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026Any-dimensional invariant universality
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Global-Aware Edge Prioritization for Pose Graph Initialization

All Graph Neural Networks papers

1,038 papers, page 3 of 21

101. ICML 2026Uncertainty-Constrained Trustworthiness for Graph Learning
102. ICML 2026Understanding Truncated Positional Encodings for Graph Neural Networks
103. ICML 2026VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
104. ICML 2026Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
105. ICML 2026Watermarking Graph Neural Networks via Explanations for Ownership Protection
106. ICML 2026What Makes a Desired Graph for Relational Deep Learning?
107. ICML 2026Which Algorithms Can Graph Neural Networks Learn?
108. ICML 2026Why Are Linear RNNs More Parallelizable?
109. ICML 2026X-EviProbe: Post-hoc Parameter-Free Evidential Uncertainty Quantification for Frozen Graph Neural Networks
110. NeurIPS 2026A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
111. NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
112. NeurIPS 2026A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
113. NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
114. NeurIPS 2026Avoiding Feature Collapse in Graph ODEs via Hysteretic Topology Evolution
115. NeurIPS 2026B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
116. NeurIPS 2026Beyond Node Sequences: Relational Diffusion for Unified Graph Learning
117. NeurIPS 2026Biological Graph Priors Enable Representation Learning for Cellular Microscopy
118. NeurIPS 2026Boosting Graph Contrastive Learning via Manifold-Guided Representation Disentanglement
119. NeurIPS 2026Bridging Graph Worlds: Neural Approximation of Gromov-Wasserstein Distances
120. NeurIPS 2026Caterpillar GNN: Replacing Message Passing with Graph-Level Aggregation
121. NeurIPS 2026Chaining 2-FWL GNNs for Combinatorial Graph Alignment
122. NeurIPS 2026Contractive Monoids: The Algebra Behind Stable Residual Propagation in Deep Graph Neural Networks
123. NeurIPS 2026Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning
124. NeurIPS 2026Cross-order Consensus Graph Matching
125. NeurIPS 2026DAG-Biased Graph Learning for Multimodal Survival on EHR
126. NeurIPS 2026Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs
127. NeurIPS 2026Do Graph Neural Networks Learn Generalizable Algorithms for Clustering Graphs?
128. NeurIPS 2026DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
129. NeurIPS 2026DualSAT: A Dual-Branch GNN-Transformer Framework for SAT Solving
130. NeurIPS 2026Dynamic Spectral Federated Graph-Level Clustering
131. NeurIPS 2026Expressive Power of Deep Homomorphism Networks over Relational Databases
132. NeurIPS 2026Faster Dynamic Graph Clustering with Hierarchical Graph Contraction
133. NeurIPS 2026Federated Graph Learning with Local Message Compensation
134. NeurIPS 2026From Link Prediction to Linear Community Detection: A Finite-Sample Guarantee for Graph Pretraining
135. NeurIPS 2026GEM: A Dual-Scale Architecture for Graph-Level Hierarchical Representation Learning
136. NeurIPS 2026Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
137. NeurIPS 2026Geometric Gain Graph: Zero-Token Graph Construction for Multi-Hop RAG
138. NeurIPS 2026GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
139. NeurIPS 2026GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architectures
140. NeurIPS 2026Gradient-Free Editing for Attribute Invariance in Graph Neural Networks
141. NeurIPS 2026Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
142. NeurIPS 2026Graph Label Alignment: A Diagnostic Atlas for Graph Classification
143. NeurIPS 2026Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
144. NeurIPS 2026Graph-Based Stochastic-Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation
145. NeurIPS 2026GraphUAT: Uncertainty Attribution in Graph Neural Networks
146. NeurIPS 2026HeterSEED: Semantics–Structure Decoupling for Heterogeneous Graph Learning under Heterophily
147. NeurIPS 2026HiddenPathQA: A Benchmark for Knowledge Graph Question Answering When Questions Hide Their Paths
148. NeurIPS 2026HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
149. NeurIPS 2026HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration
150. NeurIPS 2026IncAgg: Efficient Memory-Enhanced Graph Learning via Incremental Aggregation

FAQ

How many Graph Neural Networks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,038 in total: 434 in 2023, 208 in 2024, 213 in 2025, 183 in 2026.

How many Graph Neural Networks papers are at NeurIPS 2026?

NeurIPS 2026 has 74 Graph Neural Networks papers, versus 75 the year before.

How many Graph Neural Networks papers are at ICLR 2026?

ICLR 2026 has 28 Graph Neural Networks papers, versus 45 the year before.

How many Graph Neural Networks papers are at ICML 2026?

ICML 2026 has 68 Graph Neural Networks papers, versus 81 the year before.

How many Graph Neural Networks papers are at CVPR 2026?

CVPR 2026 has 13 Graph Neural Networks papers, versus 12 the year before.

Which conference publishes the most Graph Neural Networks papers?

NeurIPS, with 489 papers from 2023 to 2026.