多重网络回归：关系如何驱动交互？
Multiplex Network Regression: How do relations drive interactions?
摘要：
我们引入了一个统计回归模型来研究二元关系对由观察到的重复交互产生的复杂网络的影响。它是基于广义超几何集成（gHypEG），一类统计网络集成最近开发的多边缘图形和计数数据处理。我们代表不同类型的已知的系统元素之间的关系加权图，分离在不同层的多路复用网络。利用我们的方法，我们可以回归每一个关系层，解释变量，对相互作用计数，因变量的影响。此外，我们可以量化的统计意义的关系作为解释变量的观察到的相互作用。为了证明我们的方法的力量，我们调查一个基于经验数据的例子。
英文摘要：
We introduce a statistical regression model to investigate the impact of dyadic relations on complex networks generated from observed repeated interactions. It is based on generalised hypergeometric ensembles (gHypEG), a class of statistical network ensembles developed recently to deal with multi-edge graph and count data. We represent different types of known relations between system elements by weighted graphs, separated in the different layers of a multiplex network. With our method, we can regress the influence of each relational layer, the explanatory variables, on the interaction counts, the dependent variables. Moreover, we can quantify the statistical significance of the relations as explanatory variables for the observed interactions. To demonstrate the power of our approach, we investigate an example based on empirical data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
