用隐式神经表示学习连续图模型，可任意分辨率生成大图。
Scalable Implicit Graphon Learning
- 用隐式神经网络和图神经网络联合建模连续图结构
- 在合成与真实数据上优于现有方法，支持大规模图扩展
- 适合需要图数据增强或跨规模分析的研究者
图翁（Graphons）是连续的图结构模型，可生成不同规模的图。我们提出可扩展的隐式图翁学习（SIGL），结合隐式神经表示（INRs）与图神经网络（GNNs），从观测图中估计图翁。相比现有方法存在固定分辨率和可扩展性差的问题，SIGL可在任意分辨率下学习连续图翁。利用GNN确定节点顺序，提升图对齐精度。此外，我们证明了估计器的渐近一致性：更复杂的INRs与GNNs能带来一致估计。在合成与真实图数据上的实验表明，SIGL性能超越现有方法，且可有效扩展至更大规模图，适用于图数据增强等任务。
原文摘要 · Abstract (English)
Graphons are continuous models that represent the structure of graphs and allow the generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning (SIGL), a scalable method that combines implicit neural representations (INRs) and graph neural networks (GNNs) to estimate a graphon from observed graphs. Unlike existing methods, which face important limitations like fixed resolution and scalability issues, SIGL learns a continuous graphon at arbitrary resolutions. GNNs are used to determine the correct node ordering, improving graph alignment. Furthermore, we characterize the asymptotic consistency of our estimator, showing that more expressive INRs and GNNs lead to consistent estimators. We evaluate SIGL in synthetic and real-world graphs, showing that it outperforms existing methods and scales effectively to larger graphs, making it ideal for tasks like graph data augmentation.
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