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Dynamics in Graph Analysis: Adding Time as a Structure for Visual and Statistical Insight

Description

Network analyses are powerful methods for both visual analytics and machine learning but can suffer as their complexity increases. By embedding time as a structural element rather than a property, we will explore how time series and interactive analysis can be improved on Graph structures. Primarily we will look at decomposition in NLP- extracted concept graphs using NetworkX and Graph Tool.

Modeling data as networks of relationships between entities can be a powerful method for both visual analytics and machine learning; people are very good at distinguishing patterns from interconnected structures, and machine learning methods get a performance improvement when applied to graph data structures. However, as these structures become more complex or embed more information over time, both visual and algorithmic methods get messy; visual analyses suffer from the "hairball" effect, and graph algorithms require either more traversal or increased computation at each vertex. A growing area to reduce this complexity and optimize analytics is the use of interactive and subgraph techniques that model how graph structures change over time.

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