Constructing Social Networks From Binary Data

TL;DR

This paper explores how to construct social networks from binary data, emphasizing that different methods of interpretation can lead to vastly different network structures, which impacts social science analyses.

Contribution

It provides a comprehensive framework for constructing social networks from binary data and demonstrates the effects of different methods using real-world meeting data.

Findings

Different network constructions yield significantly different structures.

The choice of interpretation critically affects network analysis outcomes.

Understanding the social relationship of interest is essential for accurate network modeling.

Abstract

Much of applied network analysis concerns with studying the existing relationships between a set of agents; however, little focus has been given to the considerations of how to represent observed phenomena as a network object. In the case of physical structures such as electric grids or transportation flows, the construction of a network model is fairly straightforward as the nodes and edges usually correspond to some physical structure themselves. On the other hand, construction of a social network is much less defined; while nodes may correspond to well defined social agents, such as people or groups of people, there much more liberty in defining the relationship that edges should represent. This paper studies the intricacies of constructing a social network from data, in particular, binary data, applicable to a wide range of social science contexts. We examine several methods of constructing social networks in prior literature and discuss the methods under a common framework. Finally, using a data set of meetings among technologists in New York, we show that the different constructions of social networks arising from various interpretations of the underlying social relationships can result in vastly different network structures. These findings highlight the significance of understanding the precise relationships of interest when building a network model.