Nurisso, MarcoRaviola, MatteoTosin, Andrea2024-03-182024-03-182024-03-182024-02-0210.1017/S0956792524000020https://infoscience.epfl.ch/handle/20.500.14299/206342WOS:001154868000001In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interactions a statistical description of the graph topology, given in terms of the degree distribution embedded in a Boltzmann-type kinetic equation, is sufficient to capture the collective trends of networked interacting systems. This proves the validity of a commonly accepted heuristic assumption in statistically structured graph models, namely that the so-called connectivity of the agents is the only relevant parameter to be retained in a statistical description of the graph topology. Then, we validate our results by testing them numerically against real social network data.Physical SciencesMulti-Agent SystemsNetworked InteractionsDegree DistributionGraph-Based Kinetic EquationsBoltzmann-Type Equationstext::journal::journal article::research article