APRIL 18-21, 2017
GraphLDA: Latent Dirichlet Allocation-based Visual Exploration of Dynamic Graphs
In dynamic graph visualization and analysis, it is very challenging to visualize both the overall evolution of trends and the detailed changes of structures simultaneously. In this work, we propose a latent Dirichlet allocation (LDA) -based visual exploration method for dynamic graphs. With the LDA-based analysis, we can reveal important structures in the dynamic graph based on the extracted se- mantic topics. To gain a deeper understanding of the derived structures and their evolution, we propose a visual analytics pipeline enabling users to interpret and explore the dynamic graph. To experiment with the proposed method, we provide a visual analytics system to test with real-world data. Our case on the datasets of dynamic collaboration network has demonstrated the effectiveness of the proposed method.