Flow-Based Influence Graph Visual Summarization

Lei Shi, Hanghang Tong, Jie Tang, Chuang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
Publication statusPublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014


Other14th IEEE International Conference on Data Mining, ICDM 2014



  • influence flow
  • influence graph
  • visualization

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Shi, L., Tong, H., Tang, J., & Lin, C. (2015). Flow-Based Influence Graph Visual Summarization. In Proceedings - IEEE International Conference on Data Mining, ICDM (January ed., Vol. 2015-January, pp. 983-988). [7023434] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2014.128