深度学习 · graph neural networks论文趋势 2023–2026

主要结论

• 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
• NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是数据集与基准：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 20 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks是 0.4 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · graph neural networks论文

共 1,648 篇，第 9 / 33 页

401. ICML 2025Analytical Construction on Geometric Architectures: Transitioning from Static to Temporal Link Prediction
402. ICML 2025Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
403. ICML 2025AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization
404. ICML 2025Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
405. ICML 2025Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
406. ICML 2025Beyond Message Passing: Neural Graph Pattern Machine
407. ICML 2025Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective
408. ICML 2025Boosting Protein Graph Representations through Static-Dynamic Fusion
409. ICML 2025Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
410. ICML 2025CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
411. ICML 2025Commute Graph Neural Networks
412. ICML 2025COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
413. ICML 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
414. ICML 2025Covered Forest: Fine-grained generalization analysis of graph neural networks
415. ICML 2025CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
416. ICML 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection
417. ICML 2025De-coupled NeuroGF for Shortest Path Distance Approximations on Large Terrain Graphs
418. ICML 2025Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph
419. ICML 2025Directed Graph Grammars for Sequence-based Learning
420. ICML 2025Discrete Neural Algorithmic Reasoning
421. ICML 2025Disentangled Graph Spectral Domain Adaptation
422. ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
423. ICML 2025Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
424. ICML 2025Do We Really Need Message Passing in Brain Network Modeling?
425. ICML 2025Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
426. ICML 2025Dynamic Similarity Graph Construction with Kernel Density Estimation
427. ICML 2025EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification
428. ICML 2025EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
429. ICML 2025Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation
430. ICML 2025EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
431. ICML 2025ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
432. ICML 2025Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
433. ICML 2025Enhancing Graph Invariant Learning from a Negative Inference Perspective
434. ICML 2025Enhancing Spectral GNNs: From Topology and Perturbation Perspectives
435. ICML 2025Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
436. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
437. ICML 2025EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
438. ICML 2025Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
439. ICML 2025FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
440. ICML 2025From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers
441. ICML 2025G-Adaptivity: optimised graph-based mesh relocation for finite element methods
442. ICML 2025G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
443. ICML 2025GCAL: Adapting Graph Models to Evolving Domain Shifts
444. ICML 2025Geometric Hyena Networks for Large-scale Equivariant Learning
445. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
446. ICML 2025Graph Adaptive Autoregressive Moving Average Models
447. ICML 2025Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
448. ICML 2025Graph Generative Pre-trained Transformer
449. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
450. ICML 2025Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？

共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？

NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。

ICLR 2026 有多少篇深度学习 · graph neural networks论文？

ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。

ICML 2026 有多少篇深度学习 · graph neural networks论文？

ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。

CVPR 2026 有多少篇深度学习 · graph neural networks论文？

CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。

哪个会议的深度学习 · graph neural networks论文最多？

NeurIPS，2023–2026 年共 842 篇。