Clustering large attributed information networks: an efficient incremental computing approach

DOI10.1007/s10618-012-0263-0zbMath1259.05150OpenAlexW1987600084MaRDI QIDQ1945001

Xin Huang, Jeffrey Xu Yu, Hong Cheng, Yang Zhou

Publication date: 28 March 2013

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-012-0263-0

zbMATH Keywords

parallel computingincremental computationgraph clusteringvertex partitioningedge weightsclustering refinementrandom walk distancesSA-clusterstructural and attribute similaritiesvertex properties

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Random graphs (graph-theoretic aspects) (05C80) Graph theory (including graph drawing) in computer science (68R10)

Related Items (6)

Weighted clustering of attributed multi-graphs ⋮ Clustering data that are graph connected ⋮ Community detection in node-attributed social networks: a survey ⋮ Inc-cluster ⋮ SA-cluster ⋮ A branch-and-price procedure for clustering data that are graph connected

Uses Software

• GraphScope
• SimRank

Cites Work

• Community discovery using nonnegative matrix factorization
• Random walk with restart: fast solutions and applications
• Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
• Gaussian elimination is not optimal
• Computing Communities in Large Networks Using Random Walks

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