Drawing graphs
Change Blindness in Information Visualization: A Case Study
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Challenges in Visual Data Analysis
IV '06 Proceedings of the conference on Information Visualization
What dynamic network metrics can tell us about developer roles
Proceedings of the 2008 international workshop on Cooperative and human aspects of software engineering
The "mental map" versus "static aesthetic" compromise in dynamic graphs: a user study
AUIC '08 Proceedings of the ninth conference on Australasian user interface - Volume 76
As time goes by: integrated visualization and analysis of dynamic networks
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Effectiveness of Animation in Trend Visualization
IEEE Transactions on Visualization and Computer Graphics
How important is the "Mental map"?: an empirical investigation of a dynamic graph layout algorithm
GD'06 Proceedings of the 14th international conference on Graph drawing
Centrality metric for dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Social Network Data Analytics
A visual analytics approach to dynamic social networks
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Foundations of Multidimensional Network Analysis
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Time Scale Degree Centrality: A Time-Variant Approach to Degree Centrality Measures
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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The visualization and analysis of dynamic social networks are challenging problems, demanding the simultaneous consideration of relational and temporal aspects. In order to follow the evolution of a network over time, we need to detect not only which nodes and which links change and when these changes occur, but also the impact they have on their neighbourhood and on the overall relational structure. Aiming to enhance the perception of structural changes at both the micro and the macro level, we introduce the change centrality metric. This novel metric, as well as a set of further metrics we derive from it, enable the pair wise comparison of subsequent states of an evolving network in a discrete-time domain. Demonstrating their exploitation to enrich visualizations, we show how these change metrics support the visual analysis of network dynamics.