多层动态网络聚类及其在世界贸易数据中的应用
Multilayer-Dynamic Network Clustering with Application to World Trade Data
- Northwest University（西北大学）
- Shaanxi Normal University（陕西师范大学）
- China Southern Power Grid Artificial Intelligence Technology Co., Ltd.（中国南方电网人工智能科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对全球经济一体化下国际贸易多层动态网络社区结构识别问题，提出MuDySC方法，通过同时在时间点和层间平滑特征空间投影矩阵进行社区检测，应用于粮农组织数据，揭示了贸易结构特点及国家贸易地位变化。
AI中文摘要：
全球经济一体化的快速发展使国际贸易日益动态化和相互依存。现实世界的贸易数据集，如粮农组织数据集，可自然表示为多层动态网络，其中国家为节点，国家间贸易流为边，不同产品对应不同层。因此，如何识别多层动态贸易网络中不断演变的社区结构是重要问题。现有方法多针对静态多层或单层动态网络，多层动态网络的社区检测研究较少。本文提出新方法MuDySC（多层动态谱聚类），同时在相邻时间点和同一时间点的各层平滑特征空间投影矩阵，开发高效交替迭代算法并证明其收敛性。将该方法应用于粮农组织数据，分析揭示了进出口社区结构的明显不对称，突出了主要国家持续和变化的贸易地位。
英文摘要：
International trade data, such as the FAO dataset, can be naturally represented as \emph{multilayer-dynamic networks}, where countries are nodes, trade relationships are edges, different products correspond to different layers, and networks evolve over time. An important problem is how to identify evolving community structures in such multilayer-dynamic trade networks. Motivated by this problem, we study community detection in multilayer-dynamic networks, allowing the community structure to vary across both layers and time. We propose a novel method, \emph{MuDySC} (Multilayer-Dynamic Spectral Clustering), which smooths the eigenspace projection matrices across adjacent time points and across layers at the same time point. We develop an efficient alternating iterative algorithm and establish both global and local convergence results, with the latter allowing weaker conditions on the tuning parameters when the relevant eigenspaces are sufficiently close and the algorithm is suitably initialized. We apply MuDySC to the FAO data. The analysis reveals clear asymmetry between export and import community structures and highlights both persistent and shifting trade positions of major countries. As an extension to accommodate substantial heterogeneity across network layers, we develop TLC-MuDySC, a tensor-based layer-clustering method that first identifies structurally related layers and then applies MuDySC within the estimated layer groups.
