2011 ApoloInteractiveLargeGraphSense

• (Chau et al., 2011) ⇒ Duen Horng Chau, Aniket Kittur, Jason I. Hong, and Christos Faloutsos. (2011). “Apolo: Interactive Large Graph Sensemaking by Combining Machine Learning and Visualization.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011). doi:10.1145/2020408.2020524

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Apolo%3A+interactive+large+graph+sensemaking+by+combining+machine+learning+and+visualization%22+2011
• http://portal.acm.org/citation.cfm?doid=2020408.2020524&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ApoloInteractiveLargeGraphSense	Christos Faloutsos Duen Horng Chau Aniket Kittur Jason I. Hong			Apolo: Interactive Large Graph Sensemaking by Combining Machine Learning and Visualization				10.1145/2020408.2020524