@inproceedings{466a145b1ca14da3ad10be9db40fe17a,
title = "Flow-Based Influence Graph Visual Summarization",
abstract = "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.",
keywords = "influence flow, influence graph, visualization",
author = "Lei Shi and Hanghang Tong and Jie Tang and Chuang Lin",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICDM.2014.128",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "January",
pages = "983--988",
editor = "Ravi Kumar and Hannu Toivonen and Jian Pei and {Zhexue Huang}, Joshua and Xindong Wu",
booktitle = "Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014",
edition = "January",
}