Conference
Προβολή σε EDS

A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs
Συγγραφείς: CECCARELLO, MATTEO, PIETRACAPRINA, ANDREA ALBERTO, PUCCI, GEPPINO, Upfal, Eli
Συνεισφορές: Ceccarello, Matteo, Pietracaprina, ANDREA ALBERTO, Pucci, Geppino, Upfal, Eli
Έτος έκδοσης: 2016
Συλλογή: Padua Research Archive (IRIS - Università degli Studi di Padova)
Θεματικοί όροι: Graph Analytic, Parallel Graph Algorithm, Weighted Graph Decomposition, Weighted Diameter Approximation, MapReduce
Περιγραφή: We present a space and time efficient practical parallel algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. The core of the algorithm is a weighted graph decomposition strategy generating disjoint clusters of bounded weighted radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; moreover, for important practical classes of graphs, it runs in a number of rounds asymptotically smaller than those required by the natural approximation provided by the state-of-the-art -stepping SSSP algorithm, which is its only practical linear-space competitor in the aforementioned computational scenario. We complement our theoretical findings with an extensive experimental analysis on large benchmark graphs, which demonstrates that our algorithm attains substantial improvements on a number of key performance indicators with respect to the aforementioned competitor, while featuring a similar approximation ratio (a small constant less than 1.4, as opposed to the polylogarithmic theoretical bound).
Τύπος εγγράφου: conference object
Περιγραφή αρχείου: CD-ROM
Γλώσσα: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000391251800003; ispartofbook:Proceedings of the 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); firstpage:1; lastpage:10; numberofpages:10; https://hdl.handle.net/11577/3191093
DOI: 10.1109/IPDPS.2016.61
Διαθεσιμότητα: https://hdl.handle.net/11577/3191093
https://doi.org/10.1109/IPDPS.2016.61
Αριθμός Καταχώρησης: edsbas.352885FD
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://hdl.handle.net/11577/3191093#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
Header DbId: edsbas
DbLabel: BASE
An: edsbas.352885FD
RelevancyScore: 802
AccessLevel: 3
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 802.178283691406
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22CECCARELLO%2C+MATTEO%22">CECCARELLO, MATTEO</searchLink><br /><searchLink fieldCode="AR" term="%22PIETRACAPRINA%2C+ANDREA+ALBERTO%22">PIETRACAPRINA, ANDREA ALBERTO</searchLink><br /><searchLink fieldCode="AR" term="%22PUCCI%2C+GEPPINO%22">PUCCI, GEPPINO</searchLink><br /><searchLink fieldCode="AR" term="%22Upfal%2C+Eli%22">Upfal, Eli</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: Ceccarello, Matteo<br />Pietracaprina, ANDREA ALBERTO<br />Pucci, Geppino<br />Upfal, Eli
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2016
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Padua Research Archive (IRIS - Università degli Studi di Padova)
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Graph+Analytic%22">Graph Analytic</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+Graph+Algorithm%22">Parallel Graph Algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+Graph+Decomposition%22">Weighted Graph Decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+Diameter+Approximation%22">Weighted Diameter Approximation</searchLink><br /><searchLink fieldCode="DE" term="%22MapReduce%22">MapReduce</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: We present a space and time efficient practical parallel algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. The core of the algorithm is a weighted graph decomposition strategy generating disjoint clusters of bounded weighted radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; moreover, for important practical classes of graphs, it runs in a number of rounds asymptotically smaller than those required by the natural approximation provided by the state-of-the-art -stepping SSSP algorithm, which is its only practical linear-space competitor in the aforementioned computational scenario. We complement our theoretical findings with an extensive experimental analysis on large benchmark graphs, which demonstrates that our algorithm attains substantial improvements on a number of key performance indicators with respect to the aforementioned competitor, while featuring a similar approximation ratio (a small constant less than 1.4, as opposed to the polylogarithmic theoretical bound).
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: conference object
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: CD-ROM
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: info:eu-repo/semantics/altIdentifier/wos/WOS:000391251800003; ispartofbook:Proceedings of the 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); 30th International Parallel and Distributed Processing Symposium (IPDPS 2016); firstpage:1; lastpage:10; numberofpages:10; https://hdl.handle.net/11577/3191093
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1109/IPDPS.2016.61
– Name: URL
  Label: Availability
  Group: URL
  Data: https://hdl.handle.net/11577/3191093<br />https://doi.org/10.1109/IPDPS.2016.61
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.352885FD
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.352885FD
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/IPDPS.2016.61
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Graph Analytic
        Type: general
      – SubjectFull: Parallel Graph Algorithm
        Type: general
      – SubjectFull: Weighted Graph Decomposition
        Type: general
      – SubjectFull: Weighted Diameter Approximation
        Type: general
      – SubjectFull: MapReduce
        Type: general
    Titles:
      – TitleFull: A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: CECCARELLO, MATTEO
      – PersonEntity:
          Name:
            NameFull: PIETRACAPRINA, ANDREA ALBERTO
      – PersonEntity:
          Name:
            NameFull: PUCCI, GEPPINO
      – PersonEntity:
          Name:
            NameFull: Upfal, Eli
      – PersonEntity:
          Name:
            NameFull: Ceccarello, Matteo
      – PersonEntity:
          Name:
            NameFull: Pietracaprina, ANDREA ALBERTO
      – PersonEntity:
          Name:
            NameFull: Pucci, Geppino
      – PersonEntity:
          Name:
            NameFull: Upfal, Eli
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2016
          Identifiers:
            – Type: issn-locals
              Value: edsbas
ResultId 1