Journal article

Detecting community structure in complex networks via node similarity

  • Pan, Ying Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China - Information Network Center, Guangxi University, Nanning, China
  • Li, De-Hua Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China
  • Liu, Jian-Guo Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China - Department of Physics, University of Fribourg, Switzerland
  • Liang, Jing-Zhang Information Network Center, Guangxi University, Nanning, China
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    08.03.2010
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2010, vol. 389, no. 14, p. 2849-2857
English The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with maximum similarity to this node to form a new community. The presented method has low computational complexity because of requiring only the local information of the network, and it does not need any prior knowledge about the communities and its detection results are robust on the selection of the initial node. Some real-world and computer-generated networks are used to evaluate the performance of the presented method. The simulation results demonstrate that this method is efficient to detect community structure in complex networks, and the ZLZ metrics used in the proposed method is the most suitable one among local indices in community detection.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301662
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