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

213 papers, page 3 of 5

101. ICML 2025Learn Beneficial Noise as Graph Augmentation
102. ICML 2025Learning Condensed Graph via Differentiable Atom Mapping for Reaction Yield Prediction
103. ICML 2025Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective
104. ICML 2025Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
105. ICML 2025ML$^2$-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning
106. ICML 2025Multi-View Graph Clustering via Node-Guided Contrastive Encoding
107. ICML 2025N2GON: Neural Networks for Graph-of-Net with Position Awareness
108. ICML 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
109. ICML 2025Nonparametric Teaching for Graph Property Learners
110. ICML 2025On Explaining Equivariant Graph Networks via Improved Relevance Propagation
111. ICML 2025On Measuring Long-Range Interactions in Graph Neural Networks
112. ICML 2025On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
113. ICML 2025Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived
114. ICML 2025Positional Encoding meets Persistent Homology on Graphs
115. ICML 2025Primphormer: Efficient Graph Transformers with Primal Representations
116. ICML 2025Pruning for GNNs: Lower Complexity with Comparable Expressiveness
117. ICML 2025Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs
118. ICML 2025RelGNN: Composite Message Passing for Relational Deep Learning
119. ICML 2025Rethinking Time Encoding via Learnable Transformation Functions
120. ICML 2025ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-$k$-Cut Problems
121. ICML 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
122. ICML 2025Scalable Sobolev IPM for Probability Measures on a Graph
123. ICML 2025Self-supervised Adversarial Purification for Graph Neural Networks
124. ICML 2025Signed Laplacians for Constrained Graph Clustering
125. ICML 2025Simple Path Structural Encoding for Graph Transformers
126. ICML 2025Stable Fair Graph Representation Learning with Lipschitz Constraint
127. ICML 2025Supercharging Graph Transformers with Advective Diffusion
128. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
129. ICML 2025TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
130. ICML 2025TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
131. ICML 2025Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
132. ICML 2025TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
133. ICML 2025Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
134. ICML 2025Towards characterizing the value of edge embeddings in Graph Neural Networks
135. ICML 2025Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
136. ICML 2025What Makes a Good Feedforward Computational Graph?
137. ICML 2025WILTing Trees: Interpreting the Distance Between MPNN Embeddings
138. ICML 2025Zero-Shot Generalization of GNNs over Distinct Attribute Domains
139. NeurIPS 2025$\texttt{STRCMP}$: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization
140. NeurIPS 2025A CLT for Polynomial GNNs on Community-Based Graphs
141. NeurIPS 2025Adjusted Count Quantification Learning on Graphs
142. NeurIPS 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
143. NeurIPS 2025Association-Focused Path Aggregation for Graph Fraud Detection
144. NeurIPS 2025Bridging Theory and Practice in Link Representation with Graph Neural Networks
145. NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
146. NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
147. NeurIPS 2025DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction
148. NeurIPS 2025Effects of Dropout on Performance in Long-range Graph Learning Tasks
149. NeurIPS 2025Enforcing convex constraints in Graph Neural Networks
150. NeurIPS 2025Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games

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.