Estimation of global network statistics from incomplete data

TL;DR

This paper develops scaling methods to accurately estimate global network statistics from incomplete data without assuming a known network model, validated on simulated, empirical, and large-scale social networks like Twitter.

Contribution

It introduces novel, transparent scaling techniques for predicting network statistics from partial data, applicable across various network types without prior knowledge of their generative processes.

Findings

Accurately estimates degree distributions from partial data.

Validates methods on simulated and real-world networks.

Applies techniques to Twitter data, supporting Dunbar's hypothesis.

Abstract

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.