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

TL;DR

This paper demonstrates through agent-based simulations that spatial constraints significantly influence the structure of dynamic communication networks, affecting clustering, connectivity, and small-world properties in human groups.

Contribution

It introduces a spatially explicit agent-based model showing how physical proximity impacts network formation, contrasting with non-spatial models.

Findings

Spatial constraints increase network clustering and small-world index.

Communication range and population density are key drivers of network structure.

Neglecting spatial factors leads to inaccurate predictions of network properties.

Abstract

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 communication range. Comparing the results to a non-spatial model, we show how including spatial constraints impacts our predictions of network structure: ranged 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-constrained dynamic 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 communication, as well as dynamic models of network formation more generally.