  The social brain hypothesis postulates the increasing complexity of social
interactions as a driving force for the evolution of cognitive abilities.
Whereas dyadic and triadic relations play a basic role in defining social
behaviours and pose many challenges for the social brain, individuals in animal
societies typically belong to relatively large networks. How the structure and
dynamics of these networks also contribute to the evolution of cognition, and
vice versa, is less understood. Here we review how collective phenomena can
occur in systems where social agents do not require sophisticated cognitive
skills, and how complex networks can grow from simple probabilistic rules, or
even emerge from the interaction between agents and their environment, without
explicit social factors. We further show that the analysis of social networks
can be used to develop good indicators of social complexity beyond the
individual or dyadic level. We also discuss the types of challenges that the
social brain must cope with in structured groups, such as higher information
fluxes, originating from individuals playing different roles in the network, or
dyadic contacts of widely varying durations and frequencies. We discuss the
relevance of these ideas for primates and other animals societies.
