Bandit algorithms for social network queries

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

In many cases the best way to find a profile or a set of profiles matching some criteria in a social network is via targeted crawling. An important challenge in targeted crawling is to choose the next profile to explore. Existing heuristics for targeted crawling are usually tailored for specific search criterion and could lead to short-sighted crawling decisions. In this paper we propose and evaluate a generic approach for guiding a social network crawler that aims to provide a proper balance between exploration and exploitation based on the recently introduced variant of the Multi-Armed Bandit problem with volatile arms (VMAB). Our approach is general-purpose. In addition, it provides provable performance guarantees. Experimental results indicate that our approach compares favorably with the best existing heuristics on two different domains.

Original languageEnglish
Title of host publicationProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013
Pages148-153
Number of pages6
DOIs
StatePublished - 1 Dec 2013
Event2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013 - Washington, DC, United States
Duration: 8 Sep 201314 Sep 2013

Publication series

NameProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013

Conference

Conference2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013
Country/TerritoryUnited States
CityWashington, DC
Period8/09/1314/09/13

ASJC Scopus subject areas

  • Software

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