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VOGUE: Towards A Visual Interaction-aware Graph Query Processing Framework

Summary: VOGUE interleaves visual query construction and graph query processing, exploiting GUI latency to prune false results and prefetch candidate matches and thus reduce perceived response time. Introduces action-aware indexing and processing that anticipates user edits to enable interactive subgraph query evaluation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
209
Venue
CIDR
Year
2013
Pagerank
8.8367608e-05
Overall Rank
2,428 | 83.12%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
2,191 Visual Graph Query Construction and Refinement 2017 SIGMOD 9.325575e-05
6,313 QUBLE: Blending Visual Subgraph Query Formulation with Query Processing on Large Networks 2013 SIGMOD 5.1158493e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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