Professor in Political Science and Computer and Information Science

David Lazer

On the Interpretability of Thresholded Social Networks Primary tabs

Peer Reviewed Computer Science Conference
Publication date: 
05/2017
Authors: 
Oren Tsur
David Lazer
On the Interpretability of Thresholded Social Networks Primary tabs

Understanding the factors of network formation is a fundamental aspect in the study of social dynamics. Online activity provides us with abundance of data that allows us to reconstruct and study social networks. Statistical inference methods are often used to study network formation. Ideally, statistical inference allows the researcher to study the significance of specific factors to the network formation. One popular framework is known as Exponential Random Graph Models (ERGM) which provides principled and statistically sound interpretation of an observed network structure. Networks, however, are not always given set in stone. Often times, the network is "reconstructed" by applying some thresholds on the observed data/signals. We show that subtle changes in the thresholding have significant effects on the ERGM results, casting doubts on the interpretability of the model. In this work we present a case study in which different thresholding techniques yield radically different results that lead to contrastive interpretations. Consequently, we revisit the applicability of ERGM to threshold networks.

Research Areas TOC

Computational Social Science, 21st Century Democracy, Political Networks

Computational Social Science, Collective Cognition

DNA and the Criminal Justice System

21st Century Democracy, Political Networks