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Abstract for Seminar
In the recent years, social networks have become an important topic of attention, due to the growth of online social network sites and the availability of large social networks built from available databases (IMDB, Wikipedia, Facebook, etc.). Traditionally, social networks are visualized using node-link diagrams where people are nodes and links are ties between them. However, this representation does not scale well when the network becomes large or dense. In the last years, we have investigated the use of alternative representations for networks, relying on adjacency matrices. In this talk, I will show why matrices are a useful alternative to node-link diagrams, when they are effective and some tentative to improve them to overcome their pitfalls. I will conclude with some challenges in network visualization, in particular scalability and temporal networks.
Short Bio
Jean-Daniel Fekete is a Senior Research Scientist (DR2) at INRIA Saclay - Île-de-France, one of the leading French national research centers, located in Orsay in the University Paris-Sud. He leads the AVIZ team since 2007, which focuses on Visual Analytics. AVIZ studies the analysis and visualization of large datasets, combining machine learning approaches with information visualization and multiscale interaction techniques to help analysts explore and understand massive data. Jean-Daniel's research topics include network visualization, evaluation of information visualization systems, and toolkits for user interfaces and information visualization. His research is applied in several fields such as biology, business intelligence and social network analysis. More information is available on his web site: www.aviz.fr/~fekete