El Máster Universitario en Ingeniería y Tecnología de Sistemas Software del DSIC organiza un seminario de 6 horas de introducción sobre «Social Networks in a Graph Theory and Game Theory perspective» asociado a la asignatura «Extracción de Información Desde la Red Social» . El seminario será impartido por el profesor Jan Arne Telle de la Universidad de Bergen. El seminario se desarrollará el miércoles 11 de marzo de 17:15 a 21, y el jueves 12 de marzo de 17 a 18:30, en la Sala de Juntas del DSIC. Para la asistencia al curso es necesaria la inscripción. Las plazas son limitadas. Inscripción abierta hasta el 5 de marzo (incluido).
Formulario de inscripción
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Seminario: Social Networks in a Graph Theory and Game Theory perspective.
Drawing on ideas from economics, sociology, computer science and applied mathematics, we describe the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected. We will draw on graph theory and game theory to cover topics like the structure of the Web and its use by search engines, pricing of search engine ads, social contagion, the small-world phenomenon, rich-get-richer effects, the spread of social power and popularity, information cascades etc. The seminar is based on the textbook ‘Networks, Crowds and Markets’ by Jon Kleinberg and David Easley.
Jan Arne Telle is a Professor and member of the Algorithms Research Group at the University of Bergen in Norway. He is a computer scientist with broad research interests mainly within the areas of algorithms, computational complexity, combinatorics and its applications. Much of his work concerns the search for hidden structure in input data and the efficient use of this to solve hard computational problems. For example, a recent paper in JAIR (Journal of Artificial Intelligence Research) improves the state of the art for the model counting problem by techniques coming from modern graph decompositions. He has also worked on models for the design of efficient and scalable parallel algorithms. Recently an interest in machine learning, based on big data, has led to an investigation of machine teaching, based on carefully selected data.
