{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/connect-while-in-range-modelling-the-impact","title":"Connect-while-in-range: modelling the impact of spatial constraints on dynamic communication network structures","arxiv_id":"2410.13527","date":"2024-10-17","proceeding":null,"authors":["Niek Kerssies","Jose Segovia Martin","James Winters"],"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.","url_abs":"https://arxiv.org/abs/2410.13527v1","url_pdf":"https://arxiv.org/pdf/2410.13527v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"connect-while-in-range-modelling-the-impact","repo_url":"https://github.com/niekkerssies/Range-model","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}