In a number of application domains, one observes a sequence of network data; for example, repeated measurements between users interactions in social media platforms, financial correlation networks over time, or across subjects, as in multi-subject studies of brain connectivity. One way to analyze such data is by stacking networks into a third-order array or tensor. We propose a principal components analysis (PCA) framework for sequence network data, based on a novel decomposition for semi-symmetric tensors. We derive efficient algorithms for computing our proposed "Coupled CP" decomposition and establish estimation consistency of our approach under an analogue of the spiked covariance model with rates the same as the matrix case up to a logarithmic term. Our framework inherits many of the strengths of classical PCA and is suitable for a wide range of unsupervised learning tasks, including identifying principal networks, isolating meaningful changepoints or outliers across observations, and for characterizing the "variability network" of the most varying edges. Finally, we demonstrate the effectiveness of our proposal on simulated data and on examples from political science and financial economics. The proof techniques used to establish our main consistency results are surprisingly straight-forward and may find use in a variety of other matrix and tensor decomposition problems.
翻译:在一系列应用领域,人们观察一系列网络数据;例如,在社交媒体平台、金融关联网络或不同学科用户之间互动的反复测量,如对大脑连接的多科目研究中,在时间上或不同学科之间反复测量用户在社交媒体平台、金融关联网络中的相互作用。分析这些数据的一种方法是将网络堆叠成三阶阵列或高压。我们建议了一个主要组成部分分析框架,用于对半对称温度进行序列数据分析。我们得出高效的算法,用于计算我们提议的“混合式CP”分解,并估算我们方法的一致性,在快速变异模型模拟模型中,其比率与矩阵案例相同,直至对数术语。我们的框架继承了经典五氯苯的许多长处,适合广泛的非超常的学习任务,包括确定主要网络,将有意义的变异点或异点隔开,以及确定最不同边缘的“变异性网络”的特性。最后,我们展示我们关于模拟数据的建议的有效性,以及政治学和金融学和经济学问题实例的相似性。我们使用的证据性矩阵可以令人惊讶地确定我们的主要一致性。