Clustering-structure representative sampling from graph streams

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)
1 Downloads (Pure)


Most existing sampling algorithms on graphs (i.e., network-structured data) focus on sampling from memory-resident static graphs and assume the entire graphs are always available. However, the graphs encountered in modern applications are often too large and/or too dynamic to be processed with limited memory. Furthermore, existing sampling techniques are inadequate for preserving the inherent clustering structure, which is an essential property of complex networks. To tackle these problems, we propose a new sampling algorithm that dynamically maintains a representative sample and is capable of retaining clustering structure in graph streams at any time. Performance of the proposed algorithm is evaluated through empirical experiments using real-world networks. The experimental results have shown that our proposed CPIES algorithm can produce clustering-structure representative samples and outperforms current online sampling algorithms.

Originele taal-2Engels
TitelComplex Networks and Their Applications VI
SubtitelProceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
RedacteurenC. Cherifi, H. Cherifi, M. Karsai, M. Musulesi
Plaats van productieDordrecht
Aantal pagina's13
ISBN van elektronische versie978-3-319-72150-7
ISBN van geprinte versie978-3-319-72149-1
StatusGepubliceerd - 1 jan 2018
Evenement6th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2017) - Lyon, Frankrijk
Duur: 29 nov 20171 dec 2017
Congresnummer: 6

Publicatie series

NaamStudies in Computational Intelligence
ISSN van geprinte versie1860-949X


Congres6th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2017)
Verkorte titelCOMPLEX NETWORKS 2017

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