发表机构
Beijing Normal University; Anhui University; Hefei Normal University; Fujian Normal University（北京师范大学; 安徽大学; 合肥师范学院; 福建师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于DeepWalk的框架，通过嵌入对齐与CUSUM统计量检测动态网络结构变化，理论分析并验证其有效性。
AI 中文摘要
动态网络描述了不断演化的关系系统，其中突发的结构变化可能标志着异常事件或重要转变。检测此类变化需要区分真实的结构信号与网络观测及学习表示中的波动。我们提出了一种基于DeepWalk的框架，用于检测和定位动态网络中的结构变化。对于每个快照，我们学习低维节点嵌入，通过正交Procrustes变换将其对齐到固定参考，并通过均值池化聚合成可比较的图级向量。然后，我们构建多元累积和（CUSUM）扫描统计量以识别嵌入均值的变化。在规定的正则条件下，我们推导了原假设下随机波动的界限，以及备择假设下可靠检测和一致定位的充分条件。该分析将检测性能与网络稀疏性、谱分离、嵌入维度和变化幅度联系起来，阐明了结构信号与表示变异性之间的平衡。模拟研究证明了所提方法在多种动态网络设置中检测和定位结构变化的有效性。对国际贸易网络的应用进一步展示了其在识别贸易分配转变方面的实际用途。
英文摘要
Dynamic networks describe evolving relational systems in which abrupt structural changes may signal anomalous events or important transitions. Detecting such changes requires distinguishing genuine structural signals from fluctuations in network observations and learned representations. We propose a DeepWalk-based framework for detecting and localizing structural changes in dynamic networks. For each snapshot, we learn low-dimensional node embeddings, align them to a fixed reference using orthogonal Procrustes transformations, and aggregate them by mean pooling into comparable graph-level vectors. We then construct a multivariate cumulative sum (CUSUM) scan statistic to identify changes in the embedding mean. Under stated regularity conditions, we derive bounds on stochastic fluctuations under the null hypothesis and sufficient conditions for reliable detection and consistent localization under the alternative. The analysis connects detection performance with network sparsity, spectral separation, embedding dimension, and change magnitude, clarifying the balance between structural signals and representation variability. Simulation studies demonstrate the effectiveness of the proposed method in detecting and localizing structural changes across a range of dynamic network settings. An application to an international trade network further illustrates its practical usefulness in identifying shifts in trade allocation.
