Aaron is giving a seminar at the HCI Group in the School of Computer Science in the University of Birmingham on the 28th of March. This talk will focus on Information Visulisation of Social-* data (where * are networks, media, streams, search activity and records of information dissemination etc). In addition he will discuss how we can infer patterns of individual or collective behaviour through analysis, data mining, confirmatory and exploratory visulisation.
This talk first overviews our research in SACHI before leading into an in depth exploration of one of our key topics, namely Information Visualisation and its application to Social-* data. Information Visualisation is a research area that focuses on the use of graphical techniques to present data in an explicit form. Such static (pictures) or dynamic presentations help people formulate an understanding of data and an internal model of it for reasoning about. Such pictures of data are an external artefact supporting decision making. While sharing many of the same goals of Scientific Visualisation, Human Computer Interaction, User Interface Design and Computer Graphics, Information Visualisation focuses on the visual presentation of data without a physical or geometric form. As such it relies on research in mathematics, data mining, data structures, algorithms, graph drawing, human-computer interaction, cognitive psychology, semiotics, cartography, interactive graphics, imaging and visual design.
In this talk I will present a brief history of social-* analysis and visualisation, introduce layout algorithms we have developed for visualising such data. I will complete with a detailed case study on the layout of evolving or “dynamic graphs” extracted through SNAP, our Social Network Assembly Pipeline. SNAP operates on the premise of “social network inference” and we have studied it experimentally with the analysis of 10,000,000 record sets without explicit relations.