Efficient Estimation for Longitudinal Networks via Adaptive Merging

• URL: http://arxiv.org/abs/2211.07866v5
• Date: Mon, 1 Jul 2024 13:17:32 GMT
• Title: Efficient Estimation for Longitudinal Networks via Adaptive Merging
• Authors: Haoran Zhang, Junhui Wang,
• Abstract summary: 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. A projected descent algorithm is proposed to facilitate estimation, where upper bound of the estimation error in each iteration is established.
• Score: 21.62069959992736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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