Article: Reverse nearest neighbors in large graphs

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TitleReverse nearest neighbors in large graphs
AuthorsYiu, ML1
Papadias, D3
Mamoulis, N1
Tao, Y2
KeywordsGraphs and networks
Query processing
Spatial databases
Issue Date2006
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
CitationIeee Transactions On Knowledge And Data Engineering, 2006, v. 18 n. 4, p. 540-553 [How to Cite?]
DOI: http://dx.doi.org/10.1109/TKDE.2006.1599391
AbstractA reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph. © 2006 IEEE.
ISSN1041-4347
2011 Impact Factor: 1.657
2011 SCImago Journal Rankings: 0.081
DOIhttp://dx.doi.org/10.1109/TKDE.2006.1599391
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorYiu, ML
dc.contributor.authorPapadias, D
dc.contributor.authorMamoulis, N
dc.contributor.authorTao, Y
dc.date.accessioned2007-10-30T07:06:57Z
dc.date.available2007-10-30T07:06:57Z
dc.date.issued2006
dc.description.abstractA reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph. © 2006 IEEE.
dc.description.naturepublished_or_final_version
dc.format.extent3882120 bytes
dc.format.extent1768 bytes
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dc.format.extent4295 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.format.mimetypetext/plain
dc.format.mimetypetext/plain
dc.identifier.citationIeee Transactions On Knowledge And Data Engineering, 2006, v. 18 n. 4, p. 540-553 [How to Cite?]
DOI: http://dx.doi.org/10.1109/TKDE.2006.1599391
dc.identifier.doihttp://dx.doi.org/10.1109/TKDE.2006.1599391
dc.identifier.epage553
dc.identifier.hkuros122098
dc.identifier.issn1041-4347
2011 Impact Factor: 1.657
2011 SCImago Journal Rankings: 0.081
dc.identifier.issue4
dc.identifier.openurl
dc.identifier.scopuseid_2-s2.0-33644644150
dc.identifier.spage540
dc.identifier.urihttp://hdl.handle.net/10722/47092
dc.identifier.volume18
dc.languageeng
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
dc.publisher.placeUnited States
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering
dc.relation.referencesReferences in Scopus
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
dc.subjectGraphs and networks
dc.subjectQuery processing
dc.subjectSpatial databases
dc.titleReverse nearest neighbors in large graphs
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong
  2. City University of Hong Kong
  3. Hong Kong University of Science and Technology