Ranking Online Social Users by their Influence
| Autor: | Anastasios Giovanidis, Antoine Vendeville, Bruno Baynat, Clémence Magnien |
|---|---|
| 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), ComplexNetworks, ANR, ANR-19-CE25-0011,FairEngine,Ingénierie des plates-formes sociales équitables(2019) |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Theoretical computer science Computer Networks and Communications Computer science Markov chain 02 engineering and technology System of linear equations Measure (mathematics) [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] Ranking (information retrieval) law.invention Computer Science - Networking and Internet Architecture [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] PageRank ranking law 0202 electrical engineering electronic engineering information engineering FOS: Mathematics Electrical and Electronic Engineering Social and Information Networks (cs.SI) Networking and Internet Architecture (cs.NI) Social graph influence Computer Science - Performance model online social network Node (networking) Rank (computer programming) [INFO.INFO-WB]Computer Science [cs]/Web Probability (math.PR) 020206 networking & telecommunications Computer Science - Social and Information Networks graph Computer Science Applications Performance (cs.PF) Graph (abstract data type) Software Mathematics - Probability |
| Zdroj: | IEEE/ACM Transactions on Networking IEEE/ACM Transactions on Networking, IEEE/ACM, 2021, ⟨10.1109/TNET.2021.3085201⟩ IEEE/ACM Transactions on Networking, IEEE/ACM, 2021 |
| ISSN: | 1063-6692 |
| DOI: | 10.48550/arxiv.2107.01914 |
| Popis: | We introduce an original mathematical model to analyse the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with his/her own Wall and Newsfeed, and has his/her own individual 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 are the solution of a linear system of equations, which can be resolved iteratively. In fact, our model is very flexible with respect to the modelling assumptions. Using the probabilities derived from the solution, we define a new measure of per-user influence over the entire network, the $\Psi$-score, which combines the user position on the graph with user (re-)posting activity. In the homogeneous case where all users have the same activity rates, it is shown that a variant of the $\Psi$-score is equal to PageRank. Furthermore, we compare the new model and its $\Psi$-score against the empirical influence measured from very large data traces (Twitter, Weibo). The results illustrate that these new tools can accurately rank influencers with asymmetric (re-)posting activity for such real world applications. Comment: 18 pages, 7 figures, journal publications. arXiv admin note: text overlap with arXiv:1902.07187 |
| Databáze: | OpenAIRE |
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