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

208 papers, page 3 of 5

101. ICML 2024PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
102. ICML 2024Perfect Alignment May be Poisonous to Graph Contrastive Learning
103. ICML 2024Position: Graph Foundation Models Are Already Here
104. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
105. ICML 2024Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
106. ICML 2024Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
107. ICML 2024Quantum Positional Encodings for Graph Neural Networks
108. ICML 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
109. ICML 2024Robust Graph Matching when Nodes are Corrupt
110. ICML 2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
111. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders
112. ICML 2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
113. ICML 2024Stay on Topic with Classifier-Free Guidance
114. ICML 2024Structure Your Data: Towards Semantic Graph Counterfactuals
115. ICML 2024Surprisingly Strong Performance Prediction with Neural Graph Features
116. ICML 2024The Expressive Power of Path-Based Graph Neural Networks
117. ICML 2024Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
118. ICML 2024Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
119. ICML 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
120. NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
121. NeurIPS 2024Almost Surely Asymptotically Constant Graph Neural Networks
122. NeurIPS 2024An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval
123. NeurIPS 2024Analysis of Corrected Graph Convolutions
124. NeurIPS 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learning
125. NeurIPS 2024Are Graph Neural Networks Optimal Approximation Algorithms?
126. NeurIPS 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
127. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
128. NeurIPS 2024Boosting Graph Pooling with Persistent Homology
129. NeurIPS 2024Can Graph Learning Improve Planning in LLM-based Agents?
130. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
131. NeurIPS 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
132. NeurIPS 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
133. NeurIPS 2024Deep Graph Mating
134. NeurIPS 2024Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
135. NeurIPS 2024Deep Homomorphism Networks
136. NeurIPS 2024DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
137. NeurIPS 2024DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
138. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
139. NeurIPS 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
140. NeurIPS 2024Dynamic Rescaling for Training GNNs
141. NeurIPS 2024EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
142. NeurIPS 2024Energy-based Epistemic Uncertainty for Graph Neural Networks
143. NeurIPS 2024Enhancing Chess Reinforcement Learning with Graph Representation
144. NeurIPS 2024Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
145. NeurIPS 2024Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
146. NeurIPS 2024Even Sparser Graph Transformers
147. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
148. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
149. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
150. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization

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.