poster

Active search on graphs

Authors:

Xuezhi Wang,

Roman Garnett,

Jeff SchneiderAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 731 - 738

https://doi.org/10.1145/2487575.2487605

Published: 11 August 2013 Publication History

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Abstract

Active search is an increasingly important learning problem in which we use a limited budget of label queries to discover as many members of a certain class as possible. Numerous real-world applications may be approached in this manner, including fraud detection, product recommendation, and drug discovery. Active search has model learning and exploration/exploitation features similar to those encountered in active learning and bandit problems, but algorithms for those problems do not fit active search.

Previous work on the active search problem [5] showed that the optimal algorithm requires a lookahead evaluation of expected utility that is exponential in the number of selections to be made and proposed a truncated lookahead heuristic. Inspired by the success of myopic methods for active learning and bandit problems, we propose a myopic method for active search on graphs. We suggest selecting points by maximizing a score considering the potential impact of selecting a node, meant to emulate lookahead while avoiding exponential search. We test the proposed algorithm empirically on real-world graphs and show that it outperforms popular approaches for active learning and bandit problems as well as truncated lookahead of a few steps.

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Cited By

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Chen RImani MImani F(2021)Joint active search and neuromorphic computing for efficient data exploitation and monitoring in additive manufacturingJournal of Manufacturing Processes10.1016/j.jmapro.2021.09.04871(743-752)Online publication date: Nov-2021
https://doi.org/10.1016/j.jmapro.2021.09.048
Morales PCaceres REliassi-Rad T(2021)Selective network discovery via deep reinforcement learning on embedded spacesApplied Network Science10.1007/s41109-021-00365-86:1Online publication date: 20-Mar-2021
https://doi.org/10.1007/s41109-021-00365-8
Zhu SColes JXie Sd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Active Search using Meta-BanditsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3417409(3493-3496)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3417409
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Index Terms

Active search on graphs
1. Information systems
  1. Information retrieval

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Published In

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
,
Ramasamy Uthurusamy
General Motors Corporation (retired)
,
Program Chairs:
Inderjit S. Dhillon
University of Texas
,
Yehuda Koren
Google

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2013

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

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KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Chen RImani MImani F(2021)Joint active search and neuromorphic computing for efficient data exploitation and monitoring in additive manufacturingJournal of Manufacturing Processes10.1016/j.jmapro.2021.09.04871(743-752)Online publication date: Nov-2021
https://doi.org/10.1016/j.jmapro.2021.09.048
Morales PCaceres REliassi-Rad T(2021)Selective network discovery via deep reinforcement learning on embedded spacesApplied Network Science10.1007/s41109-021-00365-86:1Online publication date: 20-Mar-2021
https://doi.org/10.1007/s41109-021-00365-8
Zhu SColes JXie Sd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Active Search using Meta-BanditsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3417409(3493-3496)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3417409
Klyuchnikov NMottin DKoutrika GMüller EKarras PTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Figuring out the User in a Few StepsProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330893(686-695)Online publication date: 25-Jul-2019
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Madhawa KMurata T(2019)A multi-armed bandit approach for exploring partially observed networksApplied Network Science10.1007/s41109-019-0145-04:1Online publication date: 31-May-2019
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Zhuhadar LCiampa M(2019)Novel findings of hidden relationships in offshore tax-sheltered firms: a semantically enriched decision support systemJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01392-112:4(4377-4394)Online publication date: 10-Aug-2019
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Morales PCaceres REliassi-Rad T(2019)Deep Reinforcement Learning for Task-Driven Discovery of Incomplete NetworksComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_75(903-914)Online publication date: 26-Nov-2019
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Jiang SMalkomes GAbbott MMoseley BGarnett R(2018)Efficient nonmyopic batch active searchProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327045(1107-1117)Online publication date: 3-Dec-2018
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Rabbany RBayani DDubrawski AGuo YFarooq F(2018)Active Search of Connections for Case Building and Combating Human TraffickingProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220103(2120-2129)Online publication date: 19-Jul-2018
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Xin DEl-Kishky ALiao DNorick BHan J(2018)Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00184(1350-1355)Online publication date: Nov-2018
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Minimax analysis of active learning

Towards Active Learning on Graphs: An Error Bound Minimization Approach

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