Graph based visualization of large scale microblog data

Abstract

Visualization is an important but tough way to make sense of large scale dataset. In this paper, we propose a graph based method to visualize microblog data. In our scheme, the graph is constructed using the content similarities between data which is more robust than the widely used data relationships. Given a targeted dataset, we first adopt a duplicates removal strategy to reduce the size of the data and a subset is randomly sampled for visualization. Then a multilevel graph layout with a heat map is applied to generate an interactive interface which allows users to move on and scale the layout. In this way, different granularities of summarization information can be immediately presented to users when a certain area is specified in the interface; meanwhile more detailed knowledge on the selected area can be shown in nearly real time by leveraging a hash based microblog retrieval approach. Experiments are conducted on a Brand-Social-Net dataset which contains 3,000,000 microblogs and the experimental results show that, with our visualization method, some meaningful patterns of dataset can be found easily.