  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.
