Efficient Estimation for Longitudinal Networks via Adaptive Merging
通过自适应合并实现纵向网络的高效估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以纵向网络的时间边为研究对象，直接提出网络合并与网络参数估计方法。
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通过自适应合并实现纵向网络的高效估计
- 研究问题
- 对于边在时间中持续出现的稀疏纵向网络，如何借助相邻网络的信息降低估计方差，同时控制合并引入的偏误？
- 方法与思路
- 作者结合自适应网络合并、张量分解和点过程，利用局部时间结构选择合并邻域，并提出投影梯度下降估计算法。
- 材料陈述的贡献
- 摘要称，文章给出逐次迭代的估计误差上界与渐近分析，提供网络合并指引，并以合成数据和国家间军事争端数据展示效果。
- 阅读关注点
- 应核对局部时间结构和稀疏性的具体限制、合并邻域的选择规则，以及算法误差界与统计误差界分别依赖哪些条件。
- 阅读边界
- 仅据摘要，无法判断误差降低的具体幅度、适用网络规模或比较基准；网络估计结果也不直接提供因果干扰效应。
原文摘要
Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. A projected gradient descent algorithm is proposed to facilitate estimation, where the upper bound of the estimation error in each iteration is established. A thorough analysis is conducted to quantify the asymptotic behavior of the proposed method, which shows that it can significantly reduce the estimation error and also provides a guideline for network merging under various scenarios. We further demonstrate the advantage of the proposed method through extensive numerical experiments on synthetic datasets and a militarized interstate dispute dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.