Engineering High-Performance Community Detection Heuristics for Massive Graphs

The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering)...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the International Conference on Parallel Processing s. 180 - 189
Hlavní autoři: Staudt, Christian L., Meyerhenke, Henning
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2013
Témata:
Algorithm design and analysis
Benchmark testing
Clustering algorithms
Communities
Community detection
graph clustering
Graphics processing units
high-performance network analysis
Linear programming
parallel algorithm engineering
Parallel processing
ISSN:0190-3918
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism is often necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible clustering algorithm framework with shared-memory parallelism. Within this framework we implement our parallel variations of known sequential algorithms and combine them by an ensemble approach. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. The processing rate of our fastest algorithm exceeds 10M edges/second for many large graphs, making it suitable for massive data streams. Moreover, the strongest algorithm we developed yields a very good tradeoff between quality and speed.
AbstractList The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism is often necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible clustering algorithm framework with shared-memory parallelism. Within this framework we implement our parallel variations of known sequential algorithms and combine them by an ensemble approach. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. The processing rate of our fastest algorithm exceeds 10M edges/second for many large graphs, making it suitable for massive data streams. Moreover, the strongest algorithm we developed yields a very good tradeoff between quality and speed.
Author Meyerhenke, Henning
Staudt, Christian L.
Author_xml – sequence: 1
  givenname: Christian L.
  surname: Staudt
  fullname: Staudt, Christian L.
  email: christian.staudt@kit.edu
  organization: Inst. of Theor. Inf., KarInstitute of Theoretical Informaticslsruhe Inst. of Technol. (KIT), Karlsruhe, Germany
– sequence: 2
  givenname: Henning
  surname: Meyerhenke
  fullname: Meyerhenke, Henning
  email: meyerhenke@kit.edu
  organization: Inst. of Theor. Inf., KarInstitute of Theoretical Informaticslsruhe Inst. of Technol. (KIT), Karlsruhe, Germany
BookMark eNotjL1OwzAYAI1UJNrCxsbiF0jwZ8c_GVEobaUCGWCuXOdza0Scyk6R-vZUgltuOd2MTOIQkZB7YCUAqx_XTduWnIEoub4iM6ZVLSWAFhMyZVCzQtRgbsgs5y_GOBOympK3RdyHiJhC3NNV2B-KFpMfUm-jQ9oMfX-KYTzTZxzRjWGIdIWnFPIYXKaXjr7anMMP0mWyx0O-Jdfefme8-_ecfL4sPppVsXlfrpunTRFAy7FQqCpnuJI1t151zHrhwV2QRsDOeSmlqDwKszOiEx3fcYumQu1AW82cFHPy8PcNiLg9ptDbdN4qZbSQIH4BJ9NPlA
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICPP.2013.27
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISBN 0769551173
9780769551173
EndPage 189
ExternalDocumentID 6687351
Genre orig-research
GroupedDBID -~X
23M
29P
6IE
6IF
6IH
6IK
6IL
6IN
AAJGR
AAWTH
ABDPE
ADZIZ
AFFNX
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IPLJI
M43
OCL
RIE
RIL
RNS
XOL
ID FETCH-LOGICAL-i175t-6e64c826592af6d0af3f1cccc5831bcf55534fe38b83d3d2b2ae84e7c17a70c53
IEDL.DBID RIE
ISICitedReferencesCount 32
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000330046000019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0190-3918
IngestDate Wed Aug 27 03:54:27 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-6e64c826592af6d0af3f1cccc5831bcf55534fe38b83d3d2b2ae84e7c17a70c53
PageCount 10
ParticipantIDs ieee_primary_6687351
PublicationCentury 2000
PublicationDate 2013-Oct.
PublicationDateYYYYMMDD 2013-10-01
PublicationDate_xml – month: 10
  year: 2013
  text: 2013-Oct.
PublicationDecade 2010
PublicationTitle Proceedings of the International Conference on Parallel Processing
PublicationTitleAbbrev icpp
PublicationYear 2013
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0020354
ssib026763296
Score 2.0978642
Snippet The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information,...
SourceID ieee
SourceType Publisher
StartPage 180
SubjectTerms Algorithm design and analysis
Benchmark testing
Clustering algorithms
Communities
Community detection
graph clustering
Graphics processing units
high-performance network analysis
Linear programming
parallel algorithm engineering
Parallel processing
Title Engineering High-Performance Community Detection Heuristics for Massive Graphs
URI https://ieeexplore.ieee.org/document/6687351
WOSCitedRecordID wos000330046000019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZKxcBUoEW85YGRtHEu8WMulDJQZQCpWxXbZ6lLitq0Ev8eO01bkFiIZCmyPFjnx53t7_uOkAdlUVkJJmJOh2dGUJGUlkXATeHi2PkQX9fJJsRkIqdTlbfI454Lg4g1-Az74bd-y7cLsw5XZQPOpYDAlz4Sgm-5Wru5k3C_UJIgHdcctmLI0h1VGhSTe9C7GrwO8zyAuqCf_E6qUvuUUed_vTklvQM5j-Z7t3NGWliek84uOwNtFmuXTH5IDdIA54jyA0mANsSQ6os-YVXDsUo6xnWj20x9O_rm42q_F9KXoGm96pGP0fP7cBw12ROiuQ8JqogjT40_PGQqKRy3ceHAMeO_TALTxmVZBqlDkFqCBZvopECZojBMFCI2GVyQdrko8ZJQVgg_ckolyFSqldbgUAuO6H299eWKdIN5Zp9bgYxZY5nrv6tvyEkw_hYRd0va1XKNd-TYbKr5anlfj-o36U2isA
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8MgFCaLmuhp6mb8LQePdmuhUDhP5xa3poeZ7LYUeCS7dGbrTPzvha7bNPFiE5KGcCA84D3g-76H0KM0II2gOois8s-MVAZCmCigXOc2DK0L8VWVbCJJUzGdyqyBnnZcGACowGfQ8b_VW75Z6LW_KutyLhLq-dKHLI5JuGFrbWcP4W6pEC8eVx-3QsriLVmaykjsYO-yO-xlmYd10Q75nVal8ir95v_6c4rae3oeznaO5ww1oDhHzW1-Blwv1xZKf4gNYg_oCLI9TQDX1JDyCz9DWQGyCjyAda3cjF07PHaRtdsN8atXtV610Xv_ZdIbBHX-hGDugoIy4MBj7Y4PTJLcchPmltpIu48JGiltGWM0tkCFEtRQQxTJQcSQ6CjJk1AzeoEOikUBlwhHeeJsJyWBSMZKKkUtqIQDOG9vXLlCLT88s4-NRMasHpnrv6sf0PFgMh7NRsP07QadeENs8HG36KBcruEOHenPcr5a3lcW_gaPiKX3
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=Proceedings+of+the+International+Conference+on+Parallel+Processing&rft.atitle=Engineering+High-Performance+Community+Detection+Heuristics+for+Massive+Graphs&rft.au=Staudt%2C+Christian+L.&rft.au=Meyerhenke%2C+Henning&rft.date=2013-10-01&rft.pub=IEEE&rft.issn=0190-3918&rft.spage=180&rft.epage=189&rft_id=info:doi/10.1109%2FICPP.2013.27&rft.externalDocID=6687351
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0190-3918&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0190-3918&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0190-3918&client=summon