Flow-based Influence Graph Visual Summarization
Lei Shi, Hanghang Tong, Jie Tang, Chuang Lin

TL;DR
This paper introduces a novel influence graph summarization framework that visually emphasizes influence flows, maintains readability, and supports rich attributes, outperforming previous methods especially in academic citation networks.
Contribution
We formally define the influence graph summarization problem and propose an end-to-end framework that highlights influence flows while preserving graph readability and attributes.
Findings
Effective approximation of influence graph summarization objective
Outperforms previous methods in academic citation network scenarios
Supports rich graph attributes in visual summaries
Abstract
Visually mining a large influence graph is appealing yet challenging. People are amazed by pictures of newscasting graph on Twitter, engaged by hidden citation networks in academics, nevertheless often troubled by the unpleasant readability of the underlying visualization. Existing summarization methods enhance the graph 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. Our method can not only…
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