Simple Summary: The importance of animals within a social group can be ranked with the aid of centrality parameters, e.g., measures derived from social network analysis. In the present study, it was investigated whether these centrality parameters capture a similar rank order compared to dominance indices which are calculated based on the number of won and lost fights. Social networks for animals in three repeated mixing events were built (weaned piglets, fattening pigs, gilts) based on different types of interactions (in the present study, initiating and receiving agonistic interactions, and winning or losing a fight). Centrality parameters based on active behavior, especially winning an agonistic interaction, showed a similar rank order compared to the dominance indices. Also, the results of partial least squares structural equation modelling showed that the networks built on information about winning or losing a fight could best illustrate the dominance structure with an explained variance of about 60% for all three age groups. Thus, network analysis can provide information about the dominance structure within the group and also has the advantage of including indirect relationships between the animals which cannot be supported by the dyadic approach.

Abstract: Dominance indices are often calculated using the number of won and lost fights of each animal focusing on dyadic interactions. Social network analysis provides new insights into the establishment of stable group structures going beyond the dyadic approach. Thus, it was investigated whether centrality parameters describing the importance of each animal for the network are able to capture the rank order calculated by dominance indices. Therefore, two dominance indices and five centrality parameters based on two network types (initiator-receiver and winner-loser networks) were calculated regarding agonistic interactions observed in three mixing events (weaned piglets, fattening pigs, gilts). Comparing the two network types, the winner-loser networks demonstrated highly positive correlation coefficients between out-degree and outgoing closeness and the dominance indices. These results were confirmed by partial least squares structural equation modelling (PLS-SEM), i.e., about 60% of the variance of the dominance could be explained by the centrality parameters, whereby the winner-loser networks could better illustrate the dominance hierarchy with path coefficients of about 1.1 for all age groups. Thus, centrality parameters can portray the dominance hierarchy providing more detailed insights into group structure which goes beyond the dyadic approach.