We thank the authors for their new contribution to network modelling. Data reuse, encompassing methods such as bootstrapping and cross-validation, is an area that to date has largely resisted obvious and rapid development in the network context. One of the major reasons is that mimicking the original sampling mechanisms is challenging if not impossible. To avoid deleting edges and destroying some of the network structure, the resampling strategy proposed in Li et al. (2020) based on splitting node pairs rather than nodes is therefore insightful and effective. Matrix completion is the key technique involved, with its use here providing a new perspective for network analysis.

中文翻译：

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讨论“通过边缘采样进行网络交叉验证”

我们感谢作者为网络建模做出的新贡献。迄今为止，数据重用，包括自举和交叉验证之类的方法，在很大程度上已成为网络环境中明显且快速发展的领域。主要原因之一是，模仿原始采样机制即使不是不可能，也具有挑战性。为了避免删除边并破坏某些网络结构，Li等人提出了重采样策略。因此，基于分割节点对而不是节点的等式（2020）是有见地和有效的。矩阵完成是涉及的关键技术，在此使用可以为网络分析提供新的视角。