Is less more? A commentary on the practice of 'metric hacking' in animal social network analysis.

The use of social network analysis to quantify animal social relationships has increased exponentially over the last two decades. A popular aspect of social network analysis is the use of individually based network metrics. Despite the diversity of social network metrics that exist and the large number of studies that generate network metrics, little guidance exists on the number and type of metrics that should be analysed in a single study. Here, we comment on the 'hypothesize after results are known' (HARKing) phenomenon in the context of social network analysis, a practice that we term 'metric hacking' and define as the use of statistical criteria to select which metrics to use rather than a priori choice based on a research hypothesis. We identify three situations where metric hacking can occur in studies quantifying social network metrics: (1) covariance among network metrics as explanatory variables in the same model; (2) covariance among network metrics as response variables in multiple models; and (3) covariance between response and explanatory variables in the same model. We outline several quantitative and qualitative issues associated with metric hacking, provide alternative options and guidance on the appropriate use of multiple network metrics to avoid metric hacking. By increasing awareness of the use of multiple social network metrics, we hope to encourage better practice for the selection and use of social network metrics in animal social network analysis. • We outline quantitative and qualitative issues associated with metric hacking. • We provide guidance on the appropriate use of multiple network metrics. • We encourage diligent consideration of methods and reasoned justification of metrics. • Reasoned a priori choice of metrics will improve our understanding of social networks. [ABSTRACT FROM AUTHOR]