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The stability of social relationships is important to animals living ingroups, and social network analysis provides a powerful tool to helpcharacterize and understand their (in)stability and the consequences at thegroup level. However, the use of dynamic social networks is still limited inthis 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 adataset of automatically collected cognitive tests comprising more than 16Mrecords collected over 3 years. We first built a monthly aggregated temporalnetwork describing the baboon's co-presence in the cognitive testing booths. Wethen used a null model, considering the heterogeneity in the baboons' activity,to define both positive (association) and negative (avoidance) monthlynetworks. We tested social balance theory by combining these positive andnegative social networks. The results showed that the networks werestructurally balanced and that newly created edges also tended to preservesocial balance. We investigated several network metrics to gain insights intothe individual level and group level social networks long-term temporalevolution. A measure of similarity between successive monthly networks was ableto pinpoint periods of stability and instability and to show how some baboons'ego-networks remained stable while others changed radically. Our study confirmsthe prediction of social balance theory but also shows that large fluctuationsin the numbers of triads may limit its applicability to study the dynamicevolution of animal social networks. In contrast, the use of the similaritymeasure 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 thewild.

Detecting social (in)stability in primates from their temporal co-presence network