Performance Analysis of Online Social Platforms
| Autor: | Bruno Baynat, Antoine Vendeville, Anastasios Giovanidis |
|---|---|
| Přispěvatelé: | Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), IEEE |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences 050101 languages & linguistics Social graph Computer Science - Performance Theoretical computer science Markov chain Computer science 05 social sciences Linear algebra method 02 engineering and technology [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] Computer Science - Networking and Internet Architecture Performance (cs.PF) [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF] [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Robustness (computer science) Linear system analysis 0202 electrical engineering electronic engineering information engineering Social netwoks 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Markov Chain Model Opinion dynamics |
| Zdroj: | IEEE International Conference on Computer Communications (INFOCOM) 2019 IEEE International Conference on Computer Communications (INFOCOM) 2019, IEEE, Apr 2019, PARIS, France. ⟨10.1109/INFOCOM.2019.8737539⟩ INFOCOM |
| DOI: | 10.1109/INFOCOM.2019.8737539⟩ |
| Popis: | We introduce an original mathematical model to analyze the diffusion of posts within a generic online social platform. Each user of such a platform has his own Wall and Newsfeed, as well as his own self-posting and re-posting activity. As a main result, using our developed model, we derive in closed form the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other. These probabilities are the solution of a linear system of equations. Conditions of existence of the solution are provided, and two ways of solving the system are proposed, one using matrix inversion and another using fixed-point iteration. Comparisons with simulations show the accuracy of our model and its robustness with respect to the modeling assumptions. Hence, this article introduces a novel measure which allows to rank users by their influence on the social platform, by taking into account not only the social graph structure, but also the platform design, user activity (self- and re-posting), as well as competition among posts. Comment: Preliminary version of accepted paper at INFOCOM 2019 (Paris, France) |
| Databáze: | OpenAIRE |
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