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.