Visual graph query construction and refinement

Robert Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant Navathe, Hanghang Tong, Duen Horng Chau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Locating and extracting subgraphs from large network datasets is a challenge in many domains, one that often requires learning new querying languages. We will present the first demonstration of Visage, an interactive visual graph querying approach that empowers analysts to construct expressive queries, without writing complex code (see our video: https://youtu.be/l2L7Y5mCh1s). Visage guides the construction of graph queries using a data-driven approach, enabling analysts to specify queries with varying levels of specificity, by sampling matches to a query during the analyst's interaction. We will demonstrate and invite the audience to try Visage on a popular film-actor-director graph from Rotten Tomatoes.

Original languageEnglish (US)
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1587-1590
Number of pages4
VolumePart F127746
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Other

Other2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period5/14/175/19/17

Keywords

  • Graph querying
  • Interactive querying
  • Query construction

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Pienta, R., Hohman, F., Tamersoy, A., Endert, A., Navathe, S., Tong, H., & Chau, D. H. (2017). Visual graph query construction and refinement. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (Vol. Part F127746, pp. 1587-1590). Association for Computing Machinery. https://doi.org/10.1145/3035918.3056418