网络中聚类数的交叉验证估计。

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo, Japan.

Department of Mathematical and Computing Science, Tokyo Institute of Technology, 4259-G5-22, Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8502, Japan.

Sci Rep. 2017 Jun 12;7(1):3327. doi: 10.1038/s41598-017-03623-x.

Network science investigates methodologies that summarise relational data to obtain better interpretability. Identifying modular structures is a fundamental task, and assessment of the coarse-grain level is its crucial step. Here, we propose principled, scalable, and widely applicable assessment criteria to determine the number of clusters in modular networks based on the leave-one-out cross-validation estimate of the edge prediction error.

网络科学研究总结关系数据以获得更好的可解释性的方法。识别模块化结构是一项基本任务，而粗粒度级别的评估则是其关键步骤。在这里，我们提出了基于边缘预测误差的留一交叉验证估计的原则性、可扩展且广泛适用的评估标准，用于确定模块化网络中的聚类数量。