2014 IEEE International Conference on Data Mining (ICDM) (2014)
Dec. 14, 2014 to Dec. 17, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2014.128
Visually mining a large influence graph is appealing yet challenging. Existing summarization methods enhance the visualization with blocked views, but have adverse effect on the latent influence structure. How can we visually summarize a large graph to maximize influence flows? In particular, how can we illustrate the impact of an individual node through the summarization? Can we maintain the appealing graph metaphor while preserving both the overall influence pattern and fine readability? To answer these questions, we first formally define the influence graph summarization problem. Second, we propose an end-to-end framework to solve the new problem. Last, we report our experiment results. Evidences demonstrate that our framework can effectively approximate the proposed influence graph summarization objective while outperforming previous methods in a typical scenario of visually mining academic citation networks.
Visualization, Clustering algorithms, Matrix decomposition, Data mining, Linear programming, Topology, Pipelines
L. Shi, H. Tong, J. Tang and C. Lin, "Flow-Based Influence Graph Visual Summarization," 2014 IEEE International Conference on Data Mining (ICDM), Shenzhen, China, 2014, pp. 983-988.