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Network Seminar - Ranking Online Social Users by their Influence

Network Seminar - Ranking Online Social Users by their Influence

For the next Network Seminar, we are happy to welcome Anastasios Giovanidis from CNRS (LIP6), who will talk about influence on social platforms.

Ranking Online Social Users by their Influence

Abstract:In this talk I will introduce an original mathematical model to analyse the diffusion of posts within a generic online social platform. As a main result, using the 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. By combining these probabilities we get a measure of per user influence on the entire network. This constitutes a new centrality measure which is more expressive than existing ones, in the sense that it combines the user position on the graph with the user posting activity. Comparisons with simulations show the accuracy of this model and its robustness with respect to the modelling assumptions. Furthermore, its application on large data traces from real platforms asserts its validity for real world applications, and the possibilities it opens for explaining real diffusion phenomena and predicting actual user influence.

Bio Anastasios Giovanidis is a permanent researcher of the French National Center for Scientific Research (CNRS), since 2013. He is currently affiliated with the Sorbonne University - LIP6 laboratory, and previously with Telecom ParisTech, both in Paris, France. He received his diploma in electrical and computer engineering from the National Technical University of Athens, Greece, in 2005, and his Dr.-Ing. degree in wireless communications and information theory from the Technical University of Berlin, Germany, in 2010. He has held post-doctoral positions on optimisation at the Zuse Institute Berlin, and on stochastic modelling of networks at INRIA Paris. His current research interests include stochastic modelling and control of social and telecom networks, supported by data analysis and learning.

Resources: https://github.com/yokaiAG/social-platform-model

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