Social networks are ubiquitous: they allow people to collaborate, share ideas, pictures, blogs … Hence network data contains a lot of information about users: when they connect, what they like, who their friends are … The techniques of Social Network Analysis (SNA) can be used to extract from this data knowledge useful for better understanding the users (communities) and how a site’ provided resources are used or assisting them through recommendation of adequate content or friends.
I will introduce Social Network Analysis and Recommendation techniques and will present a few illustrative examples which can be used in the context of on-line education / MOOCs or any on-line collaborative site.
Françoise Soulié Fogelman has over 40 years’ experience in data mining, social network analysis and big data both in academia and industry. A former graduate from École Normale Supérieure, she holds a PhD from University of Grenoble. She was Professor at the University of Paris 11-Orsay, where she was advisor to 20 PhDs (neural networks). She then funded a startup (Mimetics) to later join Atos (as head of a data mining – data warehouse group) and Business & Décision (as Partner) where she created and headed the CRM business unit. At KXEN, she has been Vice President Innovation until the company was bought out by SAP. She is presently Professor with the School of Computer Software at Tianjin University (China), head of the Data Science team.
She has co-authored more than 130 scientific publications and 13 books. She is an expert for the European Commission, ANR (French National Research Agency), French Competitivity cluster Cap Digital and CCF Big Data Task Force (China).