Efficient Estimation for Longitudinal Networks via Adaptive Merging
This work addresses efficient estimation for longitudinal networks, which are common in online platforms but under-studied, offering a novel approach with practical guidelines for network merging.
The paper tackles the problem of estimating longitudinal networks with sparse temporal edges by proposing an adaptive merging framework that combines neighboring networks to increase observed edges while controlling bias. The method significantly reduces estimation error, with thorough asymptotic analysis and validation on synthetic and real-world datasets.
Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time. It has become ubiquitous with the rise of online social platform and e-commerce, but largely under-investigated in literature. In this paper, we propose an efficient estimation framework for longitudinal network, leveraging strengths of adaptive network merging, tensor decomposition and point process. 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 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.