Collective motion is a widely observed phenomenon in nature. Prominent examples of such behavior are swarms of insects, bird flocks, fish schools and human crowds. Synchronized movements provide various advantages to animal groups such as defense against predators, enhanced environment exploration or foraging. In human crowds, they give rise to coordinated spatio-temporal patterns such as the spontaneous spatial organization of pedestrian flows into lanes. These collective movements have been shown to emerge from local interactions between neighboring individuals. However the proximate causes of these phenomena in most biological systems in which they have been investigated are still poorly understood. We have very scarce empirical information about the type of stimuli exchanged by neighboring individuals to control their movements or about the number and position of its neighbors an individual interacts with. However, with the recent progress in video, GPS and RFID tracking technologies, high-precision datasets on moving animal and human groups are now available, thus opening the way to a fine-scale analysis of individual behaviors and the local interaction networks ensuring group cohesion and coordination. Moreover, it has recently been shown that social networks were also affecting group movements, determining the existence of strong substructures within a group that may eventually split up into separate sub-groups. The identification and characterization of effective local neighborhoods and their interplay with social networks is a key element to a better understanding of collective group movements and for the elaboration and validation of mathematical models of these phenomena.