A Study on Risk Factors Associated with Gestational Diabetes Mellitus.

This dataset contains the complete adjacency matrix and Python scripts used to construct the directed network (DMG network) and compute its topological metrics. The network represents clinical, biochemical, and behavioral variables associated with Gestational Diabetes Mellitus (GDM), based on statistically significant correlations (p < 0.05) reported in the literature.

The adjacency matrix is presented in binary format (1 = presence of a directed connection; 0 = absence) and follows a structure where rows and columns correspond to the same ordered set of variables. The Python scripts (compatible with Python 3.x) include code for network construction, visualization, and structural analyses, such as:

Degree, closeness, betweenness, and eigenvector centrality.

k-core decomposition (7-core).

Minimum Dominating Set (MDS) identification via Integer Linear Programming (ILP).

All files are provided without access restrictions and are intended to support reproducibility and further research in network-based approaches to clinical epidemiology.

本数据集包含用于构建有向网络（DMG网络）并计算其拓扑指标的完整邻接矩阵与Python脚本。该网络基于已发表文献中报告的具有统计学显著性的相关关系（p < 0.05），表征与妊娠糖尿病（Gestational Diabetes Mellitus, GDM）相关的临床、生化及行为变量。
邻接矩阵采用二元格式存储（1代表存在有向连接，0代表不存在），其行与列对应同一组有序排列的变量。适配Python 3.x版本的Python脚本涵盖网络构建、可视化及结构分析代码，具体包括：度中心性、接近中心性、中介中心性与特征向量中心性；k核分解（7核）；通过整数线性规划（Integer Linear Programming, ILP）实现的最小支配集（Minimum Dominating Set, MDS）识别。
所有文件均无访问限制，旨在支持基于网络方法的临床流行病学领域的可重复性研究与后续拓展研究。