Fred Hohman
/ CSE Ph.D. Student at GT

Visual Graph Query Construction and Refinement

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

The interface for our demonstration shows a basic query for a film with at least one actor and one director; results are shown on the right in real time. Queries are constructed in the open space (1) by placing nodes and edges. The query results are shown in a list in (2). When a node-result is clicked, a summary of feature conditions (2.1) is shown with a summary of that node’s attributes (2.2). In this example the film must have a critics’ score of “Well-rated”.

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. 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.

Materials

PDF | Video | Poster | BibTeX | Best Demo, Honorable Mention

Citation

Visual Graph Query Construction and Refinement
Robert Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant Navathe, Hanghang Tong, Duen Horn Chau
Demo, ACM International Conference on Management of Data (SIGMOD/PODS). May 14-19, 2017. Chicago, USA.
PDF | Video | Poster | BibTeX | Best Demo, Honorable Mention

BibTeX

@inproceedings{pienta2017visual,
  title={Visual Graph Query Construction and Refinement},
  author={Pienta, Robert and Hohman, Fred and Tamersoy, Acar and Endert, Alex and Navathe, Shamkant and Tong, Hanghang and Chau, Duen Horng},
  booktitle={Proceedings of the 2017 ACM International Conference on Management of Data},
  pages={1587--1590},
  year={2017},
  organization={ACM}
}