What does Big Data tell? Sampling the social network by communication channels

TL;DR

This paper investigates how sampling social networks through individual communication channels affects observed network properties, revealing that such sampling can distort degree distributions and alter network assortativity.

Contribution

It introduces a model explaining how channel-based sampling impacts social network metrics, aligning theoretical results with empirical observations.

Findings

Sampling leads to monotonically decreasing degree distributions.

Channel sampling can induce or strengthen assortativity.

Sampling effects have significant implications for social network analysis.

Abstract

Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all of them constitutes the entire social network. However, usually one has information only about one of the channels or even a part of it, which should be considered as a subset or sample of the whole. Here we introduce a model based on a natural bilateral communication channel selection mechanism, which for one channel leads to consistent changes in the network properties. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonously decreasing distribution as observed in empirical studies of single channel data. We also find that assortativity may occur or get strengthened due to the sampling method. We analyze the far-reaching consequences of our findings.