Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation to scientific discovery. However, many existing black-box approaches for network generation tend to overfit observed data while overlooking characteristic network structure, and incur substantial computational overhead at scale. These practical challenges call for synthetic network generation methods that are both efficient and capable of capturing structural properties of networks. In this paper, we introduce Synthetic Network Generation via Latent Embedding Reconstruction (SyNGLER), a general and efficient framework for synthetic network generation that builds on latent space network models. Given an observed network, SyNGLER first learns low-dimensional latent node embeddings via a latent space network model and then reconstructs the latent space by building a distribution-free generator over these embeddings. For generation, SyNGLER first samples (or resamples) node embeddings from the generator in the latent space and then produces synthetic networks using the latent space network model. Through the latent space framework, SyNGLER preserves unique characteristics in networks such as sparsity and node degree heterogeneity, while allowing for efficient training with lower computational cost than many existing deep architectures. We provide theoretical guarantees by developing consistency results on the distance between the true and synthetic edge distributions. Empirical studies further demonstrate the effectiveness of SyNGLER, which efficiently produces networks that better preserve key network characteristics such as network moments and degree distributions compared with existing approaches. Code is available at https://github.com/FeifanJiang/syngler.

翻译：网络数据广泛存在于社会科学、生物学和信息系统等领域。生成逼真的合成网络数据在从网络模拟到科学发现等场景中具有广泛应用。然而，许多现有的黑盒网络生成方法倾向于过度拟合观测数据而忽视网络结构特征，并且在规模化应用中会产生显著的计算开销。这些实际挑战要求合成网络生成方法既高效又能捕捉网络的结构属性。本文提出基于潜在嵌入重建的合成网络生成框架（SyNGLER），这是一种基于潜在空间网络模型的通用高效网络生成框架。给定观测网络，SyNGLER首先通过潜在空间网络模型学习低维潜在节点嵌入，然后通过在这些嵌入上构建无分布假设的生成器来重建潜在空间。生成阶段，SyNGLER首先从潜在空间生成器中采样（或重采样）节点嵌入，再利用潜在空间网络模型生成合成网络。通过潜在空间框架，SyNGLER在保持网络稀疏性和节点度异质性等独特特征的同时，实现了比许多现有深度架构更低的计算代价和高效训练。我们通过建立真实边分布与合成边分布之间距离的一致性结果，提供了理论保证。实验研究进一步证明了SyNGLER的有效性，与现有方法相比，它能够高效生成更好地保留网络矩和度分布等关键网络特征的网络。代码已开源至https://github.com/FeifanJiang/syngler。