基于矩阵自回归模型的动态多层网络结构分析：国家间国际互动案例研究
Structural Analysis of a Dynamic Multilayer Network via Matrix Autoregressive Models: A Case Study of International Interactions between Countries
- Universidad Nacional de Colombia（哥伦比亚国立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出用矩阵自回归模型联合分析动态多层网络，应用于ICEWS国际互动数据，发现负面言语互动主导结构重构，平均强度时间持续性最强，互惠性跨统计量影响最广。
AI中文摘要：
动态网络和多层网络已被广泛地分别研究，但它们的联合分析相对而言仍不发达。由于动态多层网络的关系信息在每个时间点$t$可以表示为张量，每一层可以通过一组结构统计量进行概括，从而产生矩阵值观测，进而形成矩阵值时间序列。为利用这一结构，我们提出使用矩阵自回归（MAR）模型，该模型同时刻画跨关系层和结构统计量的时间依赖性。我们将此框架应用于ICEWS数据集，该数据集记录了四个关系领域下国家间的国际互动，因此自然定义了一个动态多层网络。结果表明，负面言语互动（Verbal-）在后续物质互动层的结构重构中发挥突出作用，而平均强度表现出最强的时间持续性，互惠性则具有最广泛的跨统计量影响。这些发现说明了MAR模型在提供动态多层网络中时间依赖和跨层依赖的简约且可解释刻画方面的有用性。
英文摘要：
Dynamic and multilayer networks have been widely studied separately, but their joint analysis remains comparatively underdeveloped. Because the relational information of a dynamic multilayer network can be represented as a tensor at each time point $t$, each layer can be summarized through a set of structural statistics, yielding a matrix-valued observation and, consequently, a matrix-valued time series. To exploit this structure, we propose the use of matrix autoregressive (MAR) models, which simultaneously characterize temporal dependence across relational layers and structural statistics. We apply this framework to the ICEWS dataset, which records international interactions among countries under four relational domains and therefore naturally defines a dynamic multilayer network. The results indicate that negative verbal interactions (\textit{Verbal-}) play a prominent role in the subsequent structural reconfiguration of the material-interaction layers, while mean strength exhibits the strongest temporal persistence and reciprocity the broadest cross-statistic influence. These findings illustrate the usefulness of MAR models for providing a parsimonious and interpretable characterization of temporal and cross-layer dependence in dynamic multilayer networks.
