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Summarizing large influence graphs is crucial for many graph visualization and mining tasks. Classical graph clustering and compression algorithms focus on summarizing the nodes by their structural-level or attribute-level similarities, but usually are not designed to characterize the flow-level pattern which is the centerpiece of influence graphs. On the(More)
1Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China 2 Hitachi Cambridge Laboratory, Cavendish Laboratory, Cambridge CB3 0HE, United Kingdom 3 Department of Physics, Fudan University, Shanghai 200433, China 4 Collaborative Innovation Center of Quantum Matter, Beijing 100084,(More)
This paper presents VEGAS - an online system that can illustrate the influence of one scientific paper on citation networks via the influence graph summarization and visualization. The system is built over an algorithm pipeline that maximizes the rate of influence flows in the final summarization. Both visualization and interaction designs are described(More)
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