On finding common neighborhoods in massive graphs
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| Title: | On finding common neighborhoods in massive graphs |
|---|---|
| Authors: | Buchsbaum, Adam L.1 alb@research.att.com, Giancarlo, Raffaele2 raffaele@altair.math.unipa.it, Westbrook, Jeffery R.3 jwestbrook@acm.org |
| Source: | Theoretical Computer Science. Apr2003, Vol. 299 Issue 1-3, p707. 12p. |
| Subjects: | Algorithms, Sequential analysis |
| Abstract: | We consider the problem of finding pairs of vertices that share large common neighborhoods in massive graphs. We prove lower bounds on the resources needed to solve this problem on resource-bounded models of computation. In streaming models, in which algorithms can access the input only a constant number of times and only sequentially, we show that, even with randomization, any algorithm that determines if there exists any pair of vertices with a large common neighborhood must essentially store and process the input graph off line. In sampling models, in which algorithms can only query an oracle for the common neighborhoods of specified vertex pairs, we show that any algorithm must sample almost every pair of vertices for their respective common neighborhoods. [Copyright &y& Elsevier] |
| Copyright of Theoretical Computer Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 9445900 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: On finding common neighborhoods in massive graphs – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Buchsbaum%2C+Adam+L%2E%22">Buchsbaum, Adam L.</searchLink><relatesTo>1</relatesTo><i> alb@research.att.com</i><br /><searchLink fieldCode="AR" term="%22Giancarlo%2C+Raffaele%22">Giancarlo, Raffaele</searchLink><relatesTo>2</relatesTo><i> raffaele@altair.math.unipa.it</i><br /><searchLink fieldCode="AR" term="%22Westbrook%2C+Jeffery+R%2E%22">Westbrook, Jeffery R.</searchLink><relatesTo>3</relatesTo><i> jwestbrook@acm.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Theoretical+Computer+Science%22">Theoretical Computer Science</searchLink>. Apr2003, Vol. 299 Issue 1-3, p707. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We consider the problem of finding pairs of vertices that share large common neighborhoods in massive graphs. We prove lower bounds on the resources needed to solve this problem on resource-bounded models of computation. In streaming models, in which algorithms can access the input only a constant number of times and only sequentially, we show that, even with randomization, any algorithm that determines if there exists any pair of vertices with a large common neighborhood must essentially store and process the input graph off line. In sampling models, in which algorithms can only query an oracle for the common neighborhoods of specified vertex pairs, we show that any algorithm must sample almost every pair of vertices for their respective common neighborhoods. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Theoretical Computer Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=9445900 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/S0304-3975(02)00569-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 707 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Sequential analysis Type: general Titles: – TitleFull: On finding common neighborhoods in massive graphs Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Buchsbaum, Adam L. – PersonEntity: Name: NameFull: Giancarlo, Raffaele – PersonEntity: Name: NameFull: Westbrook, Jeffery R. IsPartOfRelationships: – BibEntity: Dates: – D: 18 M: 04 Text: Apr2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 03043975 Numbering: – Type: volume Value: 299 – Type: issue Value: 1-3 Titles: – TitleFull: Theoretical Computer Science Type: main |
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