图神经网络论文趋势 2023–2026

主要结论

• 图神经网络论文从 2023 年的 434 篇降到 2026 年的 183 篇，热度在回落。
• NeurIPS 是发图神经网络论文最多的会议，2023–2026 年共 489 篇，占四个会议总量的 47%。
• 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 10 篇增至 2026 年的 10 篇。
• 在「图与结构化数据」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的时间序列是 3.2 倍，图神经网络是 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 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

全部图神经网络论文

共 81 篇，第 2 / 2 页

51. ICML 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
52. ICML 2025Nonparametric Teaching for Graph Property Learners
53. ICML 2025On Explaining Equivariant Graph Networks via Improved Relevance Propagation
54. ICML 2025On Measuring Long-Range Interactions in Graph Neural Networks
55. ICML 2025On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
56. ICML 2025Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived
57. ICML 2025Positional Encoding meets Persistent Homology on Graphs
58. ICML 2025Primphormer: Efficient Graph Transformers with Primal Representations
59. ICML 2025Pruning for GNNs: Lower Complexity with Comparable Expressiveness
60. ICML 2025Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs
61. ICML 2025RelGNN: Composite Message Passing for Relational Deep Learning
62. ICML 2025Rethinking Time Encoding via Learnable Transformation Functions
63. ICML 2025ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-$k$-Cut Problems
64. ICML 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
65. ICML 2025Scalable Sobolev IPM for Probability Measures on a Graph
66. ICML 2025Self-supervised Adversarial Purification for Graph Neural Networks
67. ICML 2025Signed Laplacians for Constrained Graph Clustering
68. ICML 2025Simple Path Structural Encoding for Graph Transformers
69. ICML 2025Stable Fair Graph Representation Learning with Lipschitz Constraint
70. ICML 2025Supercharging Graph Transformers with Advective Diffusion
71. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
72. ICML 2025TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
73. ICML 2025TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
74. ICML 2025Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
75. ICML 2025TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
76. ICML 2025Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
77. ICML 2025Towards characterizing the value of edge embeddings in Graph Neural Networks
78. ICML 2025Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
79. ICML 2025What Makes a Good Feedforward Computational Graph?
80. ICML 2025WILTing Trees: Interpreting the Distance Between MPNN Embeddings
81. ICML 2025Zero-Shot Generalization of GNNs over Distinct Attribute Domains

常见问题

2023–2026 年四大会议一共有多少篇图神经网络论文？

共 1,038 篇，按年份分别是 2023 年 434 篇、2024 年 208 篇、2025 年 213 篇、2026 年 183 篇。

NeurIPS 2026 有多少篇图神经网络论文？

NeurIPS 2026 有 74 篇图神经网络论文，上一届是 75 篇。

ICLR 2026 有多少篇图神经网络论文？

ICLR 2026 有 28 篇图神经网络论文，上一届是 45 篇。

ICML 2026 有多少篇图神经网络论文？

ICML 2026 有 68 篇图神经网络论文，上一届是 81 篇。

CVPR 2026 有多少篇图神经网络论文？

CVPR 2026 有 13 篇图神经网络论文，上一届是 12 篇。

哪个会议的图神经网络论文最多？

NeurIPS，2023–2026 年共 489 篇。