Efficient Estimation for Longitudinal Networks via Adaptive Merging

TL;DR

This paper introduces an efficient estimation framework for longitudinal networks that combines adaptive merging, tensor decomposition, and point processes to improve estimation accuracy and reduce variance in real-time network analysis.

Contribution

It proposes a novel adaptive network merging method with a gradient descent algorithm, controlling bias and error, and provides theoretical analysis and empirical validation.

Findings

Reduces estimation variance by merging neighboring networks.

Controls bias through local temporal structure exploitation.

Significantly decreases estimation error in experiments.

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.