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Apolo: Interactive Large Graph Sensemaking by Combining Machine Learning and Visualization by Duen Horng (Polo) Chau, Aniket Kittur, Jason I. Hong, Christos Faloutsos is a scholarly article available to read on EtoBox.

What is Apolo: Interactive Large Graph Sensemaking by Combining Machine Learning and Visualization about?

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.

Author

Duen Horng (Polo) Chau, Aniket Kittur, Jason I. Hong, Christos Faloutsos

Publisher

ACM

Published

2011

Language

EN