Uncovering interaction patterns of multi-agent collective motion via complex network analysis

Although it is believed that many animals tend to move together as group motion has greater benefit, until now we know only a little on how individuals interact with their neighbors. In this study, we build a directed social network for large herbivores motion based on a complex network analysis framework. By calculating the basic statistics of the two networks (for two interacting species), such as reciprocity coefficient, average path length, clustering coefficient and assortativity coefficient, we find that the induced complex interaction networks (for large herbivores) have the famous “small-world” property. Moreover, our results indicate that large herbivores (deer and sheep) have a surprising long term memory of interaction relationships and their interaction preference is stable. While we focus our attention on interacting herbivores, the methods we introduce here are expected to be applicable to multi-agent systems more generally, and also to inform the development of theoretical multi-agent models - popular in engineering applications.