Rumour Spreading Depends on the Latent Geometry and Degree Distribution in Social Network Models
<|reference_start|>Rumour Spreading Depends on the Latent Geometry and Degree Distribution in Social Network Models: We study push-pull rumour spreading in small-world models for social networks where the degrees follow a power-law. In a non-geometric setting Fountoulakis, Panagiotou and Sauerwald have shown that rumours always spread fast (SODA 2012). On the other hand, Janssen and Mehrabian have found that rumours spread slowly in a spatial preferential attachment model (SIDMA 2017). We study the question systematically for the model of geometric inhomogeneous random graphs (GIRGs), which has been found to be a good theoretical and empirical fit for social networks. Our result is two-fold: with classical Euclidean geometry both slow and fast rumour spreading may occur, depending on the exponent of the power law and the prevalence of weak ties in the networks, and we fully characterise the phase boundaries between those two regimes. Depending on the parameters, fast spreading may either mean polylogarithmic time or even doubly logarithmic time. Secondly, we show that rumour spreading is always fast in a non-metric geometry. The considered non-metric geometry allows to model social connections where resemblance of vertices in a single attribute, such as familial kinship, already strongly indicates the presence of an edge. Classical Euclidean Geometry fails to capture such ties.   For some regimes in the Euclidean setting, the efficient pathways for spreading rumours differ from previously identified paths. A vertex of degree $d$ can transmit the rumour efficiently to a vertex of larger degree by a chain of length $3$, where one of the two intermediaries has constant degree, and the other has degree $d^{c}$ for some constant $c<1$.<|reference_end|>
@article{kaufmann2024rumour,
    title={Rumour Spreading Depends on the Latent Geometry and Degree Distribution
  in Social Network Models},
    author={Marc Kaufmann and Kostas Lakis and Johannes Lengler and Raghu Raman
  Ravi and Ulysse Schaller and Konstantin Sturm},
    journal={arXiv preprint arXiv:2408.01268},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01268},
    primaryClass={math.PR cs.SI math.CO}
}