Spurious inference when comparing networks

Abstract

Comparing networks is challenging. Social networks can be shaped by many factors. Failing to adequately consider non-social processes, including sampling artefacts, can lead to spurious conclusions about differences in social networks among groups. Here we demonstrate that incorrect application of statistical testing methods when comparing networks can generate very high rates of false positives. We then show that null models, specifically pre-network permutation tests, can control for non-social differences in networks and substantially reduce rates of false positives.