document: Apolo : interactive large graph sensemaking by combining machine learning and visualization We present APOLO , a system that uses a mixed-initiative approach to help people interactively explore and make sense of large network datasets . It combines visualization , rich user interaction and machine learning to engage the user in bottom-up sensemaking to gradually build up an understanding over time by starting small , rather than starting big and drilling down . APOLO helps users find relevant information by specifying exemplars , and then using a machine learning method called Belief Propagation to infer which other nodes may be of interest . We demonstrate APOLO 's usage and benefits using a Google Scholar citation graph , consisting of 83,000 articles ( nodes ) and 150,000 citations relationships . A demo video of APOLO is available at http:\/\/www.cs.cmu.edu\/~dchau\/apolo\/apolo.mp4 .
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