muxvizpy：用于多层生物网络分析的Python库
muxvizpy: a Python library for the analysis of multilayer biological networks
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中文总结 AI 辅助
muxvizpy是基于SciPy和PyTorch的Python多层生物网络分析库，扩展了muxViz功能，可降低大规模网络的内存和时间消耗，已通过数值验证且适用性得到实例证明。
中文摘要 AI 辅助
生物系统本质上是多层的：相同的实体——基因、细胞或细菌物种——同时参与性质截然不同的各类相互作用，每类相互作用都携带着单一关系视图无法捕获的互补信息。使用单层工具或将各层折叠为单网络投影来分析此类系统，会系统性地丢失层间依赖关系，可能得出关于中心性、社区结构和鲁棒性的误导性结论。多层网络形式主义解决了这一问题，而muxViz是首批用于多层网络结构分析的综合工具包之一，但它仅支持R语言的接口及密集数据结构，限制了其对大型生物网络的适用性。我们推出muxvizpy，这是一个Python库，基于SciPy和PyTorch构建的稀疏线性代数栈，重新实现并扩展了muxViz的分析目录。Muxvizpy通过统一、可组合的API提供七类功能，在合成Erdős–Rényi和Barabási–Albert多路复用网络上通过与muxViz的数值验证，在大规模场景下大幅降低了峰值内存和运行时间。我们在病毒-人类蛋白质相互作用多路复用网络上展示了其适用性，在该网络中部分结构分析原本是不可行的。muxvizpy以MIT许可免费提供，网址为this https URL，所有已实现指标的数学定义均在补充文件中提供。
英文摘要
Biological systems are inherently multilayered: the same entities---genes, cells, or bacterial species---participate simultaneously in qualitatively distinct types of interactions, each carrying complementary information that no single relational view can capture. Analysing such systems with single-layer tools, or by collapsing layers into a monoplex projection, systematically discards inter-layer dependencies and can yield misleading conclusions about centrality, community structure, and robustness. The multilayer network formalism addresses, and \texttt{muxViz} established one of the first comprehensive toolkits for its structural analysis, but its R-only interface and dense data structures limit applicability to large biological networks. We introduce \textit{muxvizpy}, a Python library that reimplements and extends the \texttt{muxViz} analytical catalogue with a sparse linear-algebra stack built on SciPy and PyTorch. Muxvizpy exposes seven categories through a unified, composable API and is numerically validated against \texttt{muxViz} on synthetic Erdős--Rényi and Barabási--Albert multiplex networks while substantially reducing peak memory and wall-clock time at scale. We illustrate its applicability on a virus--human protein-interaction multiplex in which computing some structural analysis was unfeasible. \\[2pt] muxvizpy is freely available under the MIT licence at https://github.com/CoMuNeLab/MuxVizPy. Mathematical definitions of all implemented metrics are provided in the Additional File.
