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Using simple pedestrian dynamics to generate complex temporal networks of contact Juliette Gambaudo, Centre de Physique Théorique, Aix-Marseille Université Abstract: Many empirical face-to-face contact networks have been collected across a wide diversity of contexts [1]. Although these datasets have already demonstrated their utility in several fields like epidemiology, they remain underexploited when it comes to revealing fundamental mechanisms driving social behaviours in face-to-face interactions. We propose a novel framework for generating temporal networks of contact that replicates key observables from empirical data [2]. Our hypothesis is that some of these observed features are linked to the spatial constraints of face-to-face interactions. Unlike conventional network-based approaches, our models incorporate the role of spatial constraints in shaping interaction patterns by simulating pedestrian dynamics in a 2D space and studying their contact network. Our simulations explore various dynamics, interaction mechanisms, boundary conditions, and spatial configurations to assess the geometry's impact. A key result is the power-law distribution of inter-contact durations with an exponent of -1.5, which we reproduce using three different pedestrian models: two-dimensional random walk; active Brownian particles; Vicsek model. This suggests that this property can be recovered by any pedestrian dynamics as soon as it has a random underlying mechanism. References [1] http://www.sociopatterns.org/ [2] Masoumi, R., Gambaudo, J. & Génois, M. Simple crowd dynamics to generate complex temporal contact networks (2024). arXiv:2405.06508.