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Interactive Graph Search

Summary: Defines Interactive Graph Search (IGS) for acyclic graphs, where a machine asks a human whether there is a path from a chosen node u to the target z to guide the search. Delivers algorithms with provable near-optimal query counts and matching lower bounds, plus experiments on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5697
Venue
SIGMOD
Year
2019
Pagerank
4.7178467e-05
Overall Rank
7,535 | 47.59%
DOI
10.1145/3299869.3319885

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Showing 7 of 7 citing papers.

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Showing 12 of 12 cited papers.

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

Rank Cited Paper Year Venue Pagerank
94 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00051013264
249 Crowdsourced Databases: Query Processing with People 2011 CIDR 0.00030740523
697 Human-Assisted Graph Search: It’s Okay to Ask Questions 2011 VLDB 0.00018043655
859 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00015870894
1,164 CrowdScreen: Algorithms for Filtering Data with Humans 2012 SIGMOD 0.00013564823
1,491 CDAS: A Crowdsourcing Data Analytics System 2012 VLDB 0.00011694982
2,334 Counting with the Crowd 2013 VLDB 9.0161817e-05
2,809 Deco: A System for Declarative Crowdsourcing 2012 VLDB 8.0869896e-05
3,322 iCrowd: An Adaptive Crowdsourcing Framework 2015 SIGMOD 7.2230626e-05
4,126 Waldo: An Adaptive Human Interface for Crowd Entity Resolution 2017 SIGMOD 6.4314729e-05
4,918 Top-k Sorting Under Partial Order Information 2018 SIGMOD 5.8282325e-05
11,788 CDB: Optimizing Queries with Crowd-Based Selections and Joins 2017 SIGMOD 4.1945683e-05
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