Apolo: making sense of large network data by combining rich user interaction and machine learning

@inproceedings{Chau2011ApoloMS,
  title={Apolo: making sense of large network data by combining rich user interaction and machine learning},
  author={Duen Horng Chau and Aniket Kittur and Jason I. Hong and Christos Faloutsos},
  booktitle={CHI},
  year={2011}
}
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduce Apolo, a system that uses a mixed-initiative approach - combining visualization, rich user interaction and machine learning - to guide the user to incrementally and interactively explore large network data and make sense of it. Apolo engages the user in bottom-up sensemaking to gradually build up an understanding over… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-5 of 5 references

The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis

P. Pirolli, S. Card
In Proc. IA, • 2005
View 7 Excerpts
Highly Influenced

Past, present, and future of user interface software tools

ACM Trans. Comput.-Hum. Interact. • 2000
View 4 Excerpts
Highly Influenced

The Anatomy of a Large-Scale Hypertextual Web Search Engine

Computer Networks • 1998
View 4 Excerpts
Highly Influenced

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