Connect-while-in-range: Modelling the impact of spatial constraints on dynamic network structures

Like other social animals and biological systems, human groups constantly exchange information. Network models provide a way of quantifying this process by representing the pathways of information propagation between individuals. Existing approaches to studying these networks largely hypothesize network formation to be a result of cognitive biases and choices about who to connect to. Observational data suggests, however, that physical proximity plays a major role in shaping the formation of communication networks in human groups. Here we report results from a series of agent-based simulations in which agents move around at random in a bounded 2D space and connect while within range. Comparing the results to a non-spatial model, we show how including spatial constraints impacts our predictions of network structure: range model networks are more clustered, with slightly higher degree, higher average shortest path length, a lower number of connected components and a higher small-world index. We find two important drivers of network structure in range model networks: communication range relative to environment size, and population density. These results show that neglecting spatial constraints in models of network formation makes a difference for predicted network structures. Our simulation model quantifies this part of the process of network formation, realized by simply situating individuals in an environment. The model also provides a tool to include spatial constraints in other models of human network generation, as well as dynamic models of network formation more generally.Author summary: Much like neuroscientists require a solid understanding of the connections between each neuron in order to understand cognition, we need a solid understanding of communication pathways between individuals in order to understand the collective intelligence of groups. One major difference from brains is that individuals can move around in an environment, constantly changing communication networks. We simulated this dynamic effect of movement in an environment on network structure, by simulating a population of agents moving around in a 2D coordinate space and forming temporary connections while within a specified range. These simulations allowed us to understand the formation of networks under different spatial circumstances, quantifying the impact of communication range, population size and environment size on the properties of the resulting networks. This allows to us understand part of human networks without assuming individual connection preferences, focusing instead on the basic fact that people exist and communicate in an environment.