Daniel Archambault
University Swansea, United Kingdom
http://cs.swansea.ac.uk/~csdarchambault
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Time: Monday, September 16, 2019 at 16:00 (4 pm)
Location: TU Wien (Vienna University of Technology), 4., Favoritenstraße 9-11, FAV Hörsaal 2 (HH EG 03),close to main entrance
https://www.tuwien.at/tu-wien/organisation/zentrale-services/gebaeude-und-technik/veranstaltungsservice-alt/hoersaele/fav-hoersaal-2/
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Title:
Drawing and Visualising Event-Based Dynamic Graphs
Abstract:
One of the most important types of data in data science is the graph or network. Networks encode relationships between entities: people in social network, genes in biological network, and many others forms of data. These networks are often dynamic and consist of a set of events -- edges/nodes with individual timestamps. In the complex network literature, these networks are often referred to as temporal networks. As an example, a post to a social media service creates an edge existing at a specific time and a series of posts is a series of such events. However, the majority of dynamic graph visualisations use the timeslice, a series of snapshots of the network at given times, as a basis for visualisation. In this talk, I present two recent approaches for event-based network visualisation: DynNoSlice and the Plaid. DynNoSlice is a method for embedding these networks directly in the 2D+t space-time cube along with methods to explore the contents of the cube. The Plaid is an interactive system for visualising long in time dynamic networks and interaction provenance through interactive timeslicing.
Speaker biography:
Daniel Archambault received his PhD in Computer Science from the University of British Columbia, Canada in 2008. He is a Senior Lecturer of Computer Science at Swansea University in the United Kingdom. His principle area of research is the scalable interactive visualisation of networks in both static and dynamic settings. He also has interests in many areas of information visualisation, visual analytics, text analysis and visualisation, social media analytics, and perceptual factors in visualisation.
