基于采样的大规模图聚类分析算法

Jian Peng Zhang, Hong Chang Chen, Kai Wang, Kai Jie Zhu, Ya Wen Wang

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Samenvatting

Since computational complexities of the existing methods such as classic GN algorithm are too costly to cluster large-scale graphs, this paper studies sampling algorithms of large-scale graphs, and proposes a clustering-structure representative sampling (CRS) which can effectively maintain the clustering structure of original graphs.It can produce high quality clustering-representative nodes in samples and expand according to the corresponding expansion criteria.Then, we propose a fast population clustering inference (PCI) method on the original graphs and deduce clustering assignments of the population using the clustering labels of the sampled subgraph.Experiment results show that in comparison with state-of-the-art methods, the proposed algorithm achieves better efficiency as well as clustering accuracy on large-scale graphs.

Vertaalde titel van de bijdrageA sampling-based graph clustering algorithm for large-scale networks
Originele taal-2Chinees
Pagina's (van-tot)1731-1737
Aantal pagina's7
TijdschriftTien Tzu Hsueh Pao/Acta Electronica Sinica
Volume47
Nummer van het tijdschrift8
DOI's
StatusGepubliceerd - 1 aug 2019

Trefwoorden

  • Clustering representative nodes
  • Expansion criteria
  • Graph clustering
  • Graph sampling
  • Large-scale graphs
  • Population inference

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