Modeling social networks from sampled data

TL;DR

This paper develops methods for statistical inference on social network models using sampled data, addressing practical issues like sampling bias and missing links, with applications to collaboration networks.

Contribution

It introduces a theoretical and computational framework for inference from sampled network data, including adaptive sampling designs and likelihood-based methods.

Findings

Analysis of link-tracing sampling effects on collaboration networks

Development of inference methods for incomplete network data

Typology of network data within a likelihood framework

Abstract

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.