Estimating Clustering Indexes in Data Streams
Identifieur interne : 001A57 ( Istex/Curation ); précédent : 001A56; suivant : 001A58Estimating Clustering Indexes in Data Streams
Auteurs : Luciana S. Buriol [Brésil] ; Gereon Frahling [États-Unis] ; Stefano Leonardi [Italie] ; Christian Sohler [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: We present random sampling algorithms that with probability at least 1 − δ compute a (1 ±ε)-approximation of the clustering coefficient and of the number of bipartite clique subgraphs of a graph given as an incidence stream of edges. The space used by our algorithm to estimate the clustering coefficient is inversely related to the clustering coefficient of the network itself. The space used by our algorithm to compute the number K 3,3 of bipartite cliques is proportional to the ratio between the number of K 1,3 and K 3,3 in the graph. Since the space complexity depends only on the structure of the input graph and not on the number of nodes, our algorithms scale very well with increasing graph size. Therefore they provide a basic tool to analyze the structure of dense clusters in large graphs and have many applications in the discovery of web communities, the analysis of the structure of large social networks and the probing of frequent patterns in large graphs. We implemented both algorithms and evaluated their performance on networks from different application domains and of different size; The largest instance is a webgraph consisting of more than 135 million nodes and 1 billion edges. Both algorithms compute accurate results in reasonable time on the tested instances.
Url:
DOI: 10.1007/978-3-540-75520-3_55
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: Pour aller vers cette notice dans l'étape Curation :001B74
Links to Exploration step
ISTEX:485A339ABBCCE2B9488B249981FEEA298729FE1FLe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Estimating Clustering Indexes in Data Streams</title><author><name sortKey="Buriol, Luciana S" sort="Buriol, Luciana S" uniqKey="Buriol L" first="Luciana S." last="Buriol">Luciana S. Buriol</name><affiliation wicri:level="1"><mods:affiliation>Federal University of Rio Grande do Sul, Porto Alegre, Brazil</mods:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Federal University of Rio Grande do Sul, Porto Alegre</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: buriol@inf.ufrgs.br</mods:affiliation><country wicri:rule="url">Brésil</country></affiliation></author><author><name sortKey="Frahling, Gereon" sort="Frahling, Gereon" uniqKey="Frahling G" first="Gereon" last="Frahling">Gereon Frahling</name><affiliation wicri:level="1"><mods:affiliation>Google Research, New York, United States</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Google Research, New York</wicri:regionArea></affiliation><affiliation><mods:affiliation>E-mail: gereon@google.com</mods:affiliation><wicri:noCountry code="no comma">E-mail: gereon@google.com</wicri:noCountry></affiliation></author><author><name sortKey="Leonardi, Stefano" sort="Leonardi, Stefano" uniqKey="Leonardi S" first="Stefano" last="Leonardi">Stefano Leonardi</name><affiliation wicri:level="1"><mods:affiliation>University of Rome “La Sapienza”, Rome, Italy</mods:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome “La Sapienza”, Rome</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Stefano.Leonardi@dis.uniroma1.it</mods:affiliation><country wicri:rule="url">Italie</country></affiliation></author><author><name sortKey="Sohler, Christian" sort="Sohler, Christian" uniqKey="Sohler C" first="Christian" last="Sohler">Christian Sohler</name><affiliation wicri:level="1"><mods:affiliation>Heinz Nixdorf Institute and University of Paderborn, Paderborn, Germany</mods:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Heinz Nixdorf Institute and University of Paderborn, Paderborn</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: csohler@upb.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:485A339ABBCCE2B9488B249981FEEA298729FE1F</idno><date when="2007" year="2007">2007</date><idno type="doi">10.1007/978-3-540-75520-3_55</idno><idno type="url">https://api.istex.fr/document/485A339ABBCCE2B9488B249981FEEA298729FE1F/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001B74</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001B74</idno><idno type="wicri:Area/Istex/Curation">001A57</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Estimating Clustering Indexes in Data Streams</title><author><name sortKey="Buriol, Luciana S" sort="Buriol, Luciana S" uniqKey="Buriol L" first="Luciana S." last="Buriol">Luciana S. Buriol</name><affiliation wicri:level="1"><mods:affiliation>Federal University of Rio Grande do Sul, Porto Alegre, Brazil</mods:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Federal University of Rio Grande do Sul, Porto Alegre</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: buriol@inf.ufrgs.br</mods:affiliation><country wicri:rule="url">Brésil</country></affiliation></author><author><name sortKey="Frahling, Gereon" sort="Frahling, Gereon" uniqKey="Frahling G" first="Gereon" last="Frahling">Gereon Frahling</name><affiliation wicri:level="1"><mods:affiliation>Google Research, New York, United States</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Google Research, New York</wicri:regionArea></affiliation><affiliation><mods:affiliation>E-mail: gereon@google.com</mods:affiliation></affiliation></author><author><name sortKey="Leonardi, Stefano" sort="Leonardi, Stefano" uniqKey="Leonardi S" first="Stefano" last="Leonardi">Stefano Leonardi</name><affiliation wicri:level="1"><mods:affiliation>University of Rome “La Sapienza”, Rome, Italy</mods:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome “La Sapienza”, Rome</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Stefano.Leonardi@dis.uniroma1.it</mods:affiliation><country wicri:rule="url">Italie</country></affiliation></author><author><name sortKey="Sohler, Christian" sort="Sohler, Christian" uniqKey="Sohler C" first="Christian" last="Sohler">Christian Sohler</name><affiliation wicri:level="1"><mods:affiliation>Heinz Nixdorf Institute and University of Paderborn, Paderborn, Germany</mods:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Heinz Nixdorf Institute and University of Paderborn, Paderborn</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: csohler@upb.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2007</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">485A339ABBCCE2B9488B249981FEEA298729FE1F</idno><idno type="DOI">10.1007/978-3-540-75520-3_55</idno><idno type="ChapterID">55</idno><idno type="ChapterID">Chap55</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We present random sampling algorithms that with probability at least 1 − δ compute a (1 ±ε)-approximation of the clustering coefficient and of the number of bipartite clique subgraphs of a graph given as an incidence stream of edges. The space used by our algorithm to estimate the clustering coefficient is inversely related to the clustering coefficient of the network itself. The space used by our algorithm to compute the number K 3,3 of bipartite cliques is proportional to the ratio between the number of K 1,3 and K 3,3 in the graph. Since the space complexity depends only on the structure of the input graph and not on the number of nodes, our algorithms scale very well with increasing graph size. Therefore they provide a basic tool to analyze the structure of dense clusters in large graphs and have many applications in the discovery of web communities, the analysis of the structure of large social networks and the probing of frequent patterns in large graphs. We implemented both algorithms and evaluated their performance on networks from different application domains and of different size; The largest instance is a webgraph consisting of more than 135 million nodes and 1 billion edges. Both algorithms compute accurate results in reasonable time on the tested instances.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Rhénanie/explor/UnivTrevesV1/Data/Istex/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001A57 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Curation/biblio.hfd -nk 001A57 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Rhénanie
|area= UnivTrevesV1
|flux= Istex
|étape= Curation
|type= RBID
|clé= ISTEX:485A339ABBCCE2B9488B249981FEEA298729FE1F
|texte= Estimating Clustering Indexes in Data Streams
}}
| This area was generated with Dilib version V0.6.31. |  |