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通过半对称张量主成分分析处理多网络数据AI 解读

Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between subjects or features of interest. In an increasing number of domains, these networks are collected over time, such as repeated interactions between users of a social media platform, or across multiple subjects, such as in multi-subject neuroimaging studies. When analyzing multiple large networks, dimensionality reduction…

高维马尔可夫随机场模型的变点估计AI 解读

Summary The paper investigates a change point estimation problem in the context of high dimensional Markov random-field models. Change points represent a key feature in many dynamically evolving network structures. The change point estimate is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the…