Information Visualisation is a research area that focuses on the use of graphical techniques to present abstract 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 Aaron will present a brief history of social-network analysis and visualisation, introduce analysis and layout algorithms we have developed for visualising such data. Our recent analysis focuses on actor identification through network tuning and our Social Network Assembly Pipeline, SNAP which operates on the premise of “social network inference” where we have studied it experimentally with the analysis of 10,000,000 record sets without explicit relations. Our visulisation has focussed on large scale node-link diagrams, small multiples, dynamic network displays and egocentric layouts.  The talk concludes with a number of challenges and open research questions we face as researchers in using visualisation in an attempt to present dynamic data sources.