  The stability of social relationships is important to animals living in
groups, and social network analysis provides a powerful tool to help
characterize and understand their (in)stability and the consequences at the
group level. However, the use of dynamic social networks is still limited in
this context as it requires long-term social data and new analytical tools.
Here, we study the dynamic evolution of a group of 29 Guinea baboons using a
dataset of automatically collected cognitive tests comprising more than 16M
records collected over 3 years. We first built a monthly aggregated temporal
network describing the baboon's co-presence in the cognitive testing booths. We
then used a null model, considering the heterogeneity in the baboons' activity,
to define both positive (association) and negative (avoidance) monthly
networks. We tested social balance theory by combining these positive and
negative social networks. The results showed that the networks were
structurally balanced and that newly created edges also tended to preserve
social balance. We investigated several network metrics to gain insights into
the individual level and group level social networks long-term temporal
evolution. A measure of similarity between successive monthly networks was able
to pinpoint periods of stability and instability and to show how some baboons'
ego-networks remained stable while others changed radically. Our study confirms
the prediction of social balance theory but also shows that large fluctuations
in the numbers of triads may limit its applicability to study the dynamic
evolution of animal social networks. In contrast, the use of the similarity
measure proved to be very versatile and sensitive in detecting relationships'
(in)stabilities at different levels. The changes we identified can be linked,
at least in some cases, to females changing primary male, as observed in the
wild.
