  Network models describe the brain as sets of nodes and edges that represent
its distributed organization. So far, most discoveries in network neuroscience
have prioritized insights that highlight distinct groupings and specialized
functional contributions of network nodes. Importantly, these functional
contributions are determined and expressed by the web of their
interrelationships, formed by network edges. Here, we underscore the important
contributions made by brain network edges for understanding distributed brain
organization. Different types of edges represent different types of
relationships, including connectivity and similarity among nodes. Adopting a
specific definition of edges can fundamentally alter how we analyze and
interpret a brain network. Furthermore, edges can associate into collectives
and higher-order arrangements, describe time series, and form edge communities
that provide insights into brain network topology complementary to the
traditional node-centric perspective. Focusing on the edges, and the
higher-order or dynamic information they can provide, discloses previously
underappreciated aspects of structural and functional network organization.
