Flow-based Influence Graph Visual Summarization

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 highlight the flow-based influence patterns in the visual summarization, but also inherently support rich graph attributes. Last, we present a theoretic analysis and report our experiment results. Both 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.