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Scalable Graph Clustering and Its Applications

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Encyclopedia of Social Network Analysis and Mining

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Synonyms

Cluster analysis; Community detection; Modularity

Glossary

Cluster:

A group of densely interconnected nodes

Community Detection:

A function of network analysis that identifies groups of densely connected nodes.

Graph:

A set of nodes and edges connecting the nodes

Hub:

A special role of node that bridges multiple clusters

Network:

A graph extended with semantics and interactions between nodes and edges, respectively

Outlier:

A special role of node that is not hub and does not belong to any clusters. In many cases outliers are regarded as noises

Partition:

Division of nonoverlapping subsets

Definition

Graph is one of the fundamental data structures and we can easily find graphs in many applications and services. Graph cluster analysis is a key technique to understand structures, characteristics, and interrelationships graphs. The problem of the graph cluster analysis is to find clusters inside of which nodes are densely connected and sparsely connected inter clusters, and this...

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Author information

Authors and Affiliations

  1. Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan

    Hiroaki Shiokawa

  2. Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

    Makoto Onizuka

Authors
  1. Hiroaki Shiokawa
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  2. Makoto Onizuka
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Corresponding author

Correspondence to Hiroaki Shiokawa .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. Arizona State University, Tempe, AZ, USA

    Huan Liu

  2. Chief Data Scientist, Clari Inc., 94086, Sunnyvale, CA, USA

    Lei Tang

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Shiokawa, H., Onizuka, M. (2017). Scalable Graph Clustering and Its Applications. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_110185-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_110185-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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