解决高维数据网络推断的样本不足问题，提升结果可解释性。
A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data
- 基于重采样技术融合多种策略，增强网络推断鲁棒性。
- 首次实现稀疏网络中符号图谱度向量矩阵的近常数时间计算。
- 适合生物、金融等高维混合数据领域的网络建模与分析。
RSNet 是一个开源 R 包，提供基于重采样的框架，用于在高维数据中进行稳健且可解释的网络推断，以应对常见于高维数据中的小样本挑战。它支持通过高斯网络建模偏相关网络，以及处理连续与离散变量混合数据的条件高斯贝叶斯网络。该框架集成多种重采样策略，包括自助法（bootstrap）、子采样（subsampling）和基于聚类的方法，可适应独立与相关观测。为提升可解释性，RSNet 引入基于图谱的拓扑分析，捕捉高阶连通性与边符号信息，实现单节点及子网层面的洞察。值得注意的是，它是首个能对稀疏网络高效构建符号图谱度向量矩阵（GDVMs）并在近常数时间内完成的 R 包，实现了高阶网络结构的可扩展分析。总体而言，RSNet 为高维数据中的统计可靠、可解释的网络推断提供了多功能工具。
原文摘要 · Abstract (English)
RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the limited-sample-size challenges common in high-dimensional data. It supports both the estimation of partial correlation networks modeled as Gaussian networks and conditional Gaussian Bayesian networks for mixed data types that combine continuous and discrete variables. The framework incorporates multiple resampling strategies, including bootstrap, subsampling, and cluster-based approaches, to accommodate both independent and correlated observations. To enhance interpretability, RSNet integrates graphlet-based topology analysis that captures higher-order connectivity and edge sign information, enabling single-node and subnetwork-level insights. Notably, RSNet is the first R package to efficiently construct signed graphlet degree vector matrices (GDVMs) in near-constant time for sparse networks, providing scalable analysis of higher-order network structure. Collectively, RSNet offers a versatile tool for statistically reliable and interpretable network inference in high-dimensional data.
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