2021/05/19 by Eberhard Mayerhofer, Mayerhofer, Eberhard
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #60J85 #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Game Theory and Applications #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Probability (math.PR) #Social and Information Networks (cs.SI) #cs.SI #math.PR #msc:60J85 #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2105.09359
arxiv created 2021/05/19 · openalex publication_date 2021/05/19 · arxiv updated 2021/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
I find that several models for information sharing in social networks can be interpreted as age-dependent multi-type branching processes, and build them independently following Sewastjanow. This allows to characterize criticality in (real and random) social networks. For random networks, I develop a moment-closure method that handles the high-dimensionality of these models: By modifying the timing of sharing with followers, all users can be represented by a single representative, while leaving the total progeny unchanged. Thus I compute the exact popularity distribution, revealing a viral character of critical models expressed by fat tails of order minus three half.