AI 中文总结
该研究开发了基于动态网络序列的子图密度推断统计方法，解决了含噪动态网络分析的关键问题，扩展了含噪网络的分析场景并支持多时间点子图密度的联合统计比较。
AI 中文摘要
本研究开发了统计方法，用于利用带时间索引的动态网络序列估计子图密度并进行推断。这些估计值明确调整了网络边的观测误差，且在网络规模增大时具有良好的理论性质。通过指定随机演化的隐马尔可夫网络模型，我们解决了Chang等人（2022）确定的两个重要研究方向：对非相同网络重复样本的鲁棒性，以及对多个可用网络快照的高效聚合。这些新方法将含噪网络的分析大幅扩展至新的数据场景，因为网络重复样本常以动态形式出现。该方法还被扩展至考虑多个时间点子图密度的联合推断，以促进对动态网络快照的正式统计比较。
英文摘要
In this work we develop statistical methodology to estimate and perform inference on subgraph densities using time-indexed, or dynamic network sequences. These estimates explicitly adjust for observation errors for the network edges, and have good theoretical properties as the size of the network grows. By specifying a stochastically evolving hidden Markov network model, we address two important directions for further investigation identified by Chang et al. (2022): robustness to non-identical network replicates, and efficient aggregation of multiple available network snapshots. These new methods vastly expand the analysis of noisy networks to new data settings, as network replicates are commonly observed dynamically. The methodology is also extended to consider joint inference for subgraph densities at multiple time points, to facilitate formal statistical comparison of dynamic network snapshots.
Comments78 pages, 10 figures
