This talk provides a brief introduction to statistical network analysis and random graph models. We then focus on the problem of estimating the graphon function, which characterizes nonparametric exchangeable random graph models. Our main emphasis is on the setting where multiple networks are observed, which introduces additional challenges compared to the classical single-network framework. To address this, we propose a new histogram-based estimator with low computational complexity. The key idea is to jointly align the nodes across all observed graphs, rather than processing each network independently as in most existing approaches. We establish consistency results for the proposed estimator and demonstrate through numerical experiments that it outperforms current methods in both estimation accuracy and computational efficiency. Finally, we show that, when used for data augmentation in graph neural network classification tasks, our approach leads to improved performance on various real-world datasets. This is joint work with Roland Sogan.