The consequences of unidentifiable individuals for the analysis of an animal social network

• We investigate the sampling of an animal social network using random node sampling.
• Node level metrics in partial networks predict an animal's real social position.
• The accuracy of node level metric values depended on the social measure used.
• Indicates potential for social network analysis in large long-term study populations.

Social network analysis is pervasive in understanding animal social systems, and provides information about how individuals vary in their social strategies. Many long-term studies comprising uniquely marked individuals use social network analysis as an analytical tool. However, the assumption that it is possible to make inferences using network metrics calculated using a subset of the population has yet to be investigated in an animal social network. We use a simulation study of networks derived from social interactions in a typical fluid fission–fusion social system to determine the precision and accuracy of measures of individual social position based on incomplete knowledge. We show that individual social positions measured in partial social networks correlate strongly with positions in the full social network. This correlation typically becomes stronger as the size of the simulated population is increased and is largely not affected by network density. The choice of network metric has an important effect on the precision of partial networks only when they include a small subset of the population and therefore caution is advised using some of these measures in small partial networks. This work demonstrates that valid inferences about individual social position and strategy can be made using partial networks in a wide range of animal social networks, highlighting the value of applying these methods in large long-term study populations.