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Visual Analytics for Network-Based Market Surveillance

Time series of graphs are increasingly prevalent in modern economic and financial data and pose unique challenges to visual exploration and pattern extraction. This paper describes the application of matrix factorizations that enhance existing visualization techniques for exploration and pattern detection in graph time-series. The combination of matrix factorization and visualizations allows the user to home in on and display interesting, underlying structure and its evolution over time. The methods are scalable to data sets with a large number of time points or nodes, and can accommodate sudden changes to graph topology. The tools are used to summarize how dynamics in the interbank and equity markets changed during the sub-prime crisis for banks in the Eurozone area.