2011-apolo-interactive-large-graph-sensemaking-by-combining-machine-learning-and-visualization.md

authors	link	tags	title	venue	year
Duen Horng (Polo) Chau Aniket Kittur Jason I. Hong Christos Faloutsos		Evaluation Of Retrieval Results Relevance Assessment Human-centered Computing Information Systems Human Computer Interaction (hci) Information Retrieval	Apolo: interactive large graph sensemaking by combining machine learning and visualization.	KDD	2011

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.