Abstract
The paper presents a semantic and dynamic model providing predictive recommendations and trend predictions, based on the analysis of social networks and social media. It introduces a significant completion of the interdisciplinary paradigm based on the analogy of information flows and current, commonly used in social networks analysis. It defines a social graph structure entailing knowledge engineering, which contributes to semantic clustering and classification of the Social Web and participates in a model of predictive recommendations based on natural laws in electro-physics. These multidisciplinary contributions are applied to team performance and social climate optimization, within collaborative organizations (patented model). Outcomes are presented in line with the Socioprise project, funded by the French State Secretariat at the prospective and development of the digital economy.
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Thovex, C., Trichet, F. (2013). An Epistemic Equivalence for Predictive Social Networks Analysis. In: Haller, A., Huang, G., Huang, Z., Paik, Hy., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2011 and 2012 Workshops. WISE WISE 2011 2012. Lecture Notes in Computer Science, vol 7652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38333-5_21
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DOI: https://doi.org/10.1007/978-3-642-38333-5_21
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