Monitoring network changes in social media

Chen, C. Y.-H. , Okhrin, Y. and Wang, T. (2021) Monitoring network changes in social media. Journal of Business and Economic Statistics, (doi: 10.1080/07350015.2021.2016425) (Early Online Publication)

Abstract

Econometricians are increasingly working with high-dimensional networks and their dynamics. Econometricians, however, are often confronted with unforeseen changes in network dynamics. In this paper, we develop a method and the corresponding algorithm for monitoring changes in dynamic networks. We characterize two types of changes, edge-initiated and node-initiated, to feature the complexity of networks. The proposed approach accounts for three potential challenges in the analysis of networks. First, networks are high-dimensional objects causing the standard statistical tools to suffer from the curse of dimensionality. Second, any potential changes in social networks are likely driven by a few nodes or edges in the network. Third, in many dynamic network applications such as monitoring network connectedness or its centrality, it will be more practically applicable to detect the change in an online fashion than the offline version. The proposed detection method at each time point projects the entire network onto a low-dimensional vector by taking the sparsity into account, then sequentially detects the change by comparing consecutive estimates of the optimal projection direction. As long as the change is sizeable and persistent, the projected vectors will converge to the optimal one, leading to a jump in the sine angle distance between them. A change is therefore declared. Strong theoretical guarantees on both the false alarm rate and detection delays are derived in a sub-Gaussian setting, even under spatial and temporal dependence in the data stream. Numerical studies and an application to the social media messages network support the effectiveness of our method.

• Item Type: Articles
• Additional Information: Cathy Yi-Hsuan Chen appreciates the financial support from the German Research Foundation (DFG), Germany via the International Research Training Group 1792 “High Dimensional Nonstationary Time Series” in Humboldt-Universität zu Berlin. Yarema Okhrin thanks German Research Foundation (DFG), Germany for the financial support via DFG 103/6-1 “Empirical Similarity: estimation, multivariate extensions, and applications”. Tengyao Wang appreciates the financial support of EPSRC grant EP/T02772X/1.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Chen, Professor Cathy Yi-Hsuan
• Authors: Chen, C. Y.-H., Okhrin, Y., and Wang, T.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Journal of Business and Economic Statistics
• Publisher: Taylor and Francis
• ISSN: 0735-0015
• ISSN (Online): 1537-2707
• Published Online: 13 December 2021
• Copyright Holders: Copyright © 2021 American Statistical Association
• First Published: First published in Journal of Business and Economic Statistics 2021
• Publisher Policy: Reproduced in accordance with the publisher copyright policy