Open AccessEfficient community detection method based on attribution of nodes in complex network
Author(s) -
Cai Biao,
Sang Qiang,
Zeng Lina,
Wu Jiang
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8295
Subject(s) - node (physics) , computer science , attribution , community structure , enhanced data rates for gsm evolution , belongingness , complex network , network science , data mining , data science , computer network , artificial intelligence , world wide web , mathematics , psychology , social psychology , engineering , statistics , structural engineering
The research of network science is of great significance to the study of human society. The discovery of community structure in a network is an important research direction in the network science. Based on the research of traditional community discovery, this study found that sporadic nodes at the edge of a network only belong to the community to which the node is connected. Therefore, the community attributions of these sporadic edge nodes are stronger than that of the network central nodes. Based on this finding, the authors proposed a community discovery method based on the calculation of network node belongingness. In this proposed method, the community detection is first carried out based on the node attributions. Second, a simple method that determines the number of communities is defined. At last, the communities are optimally combined according to the average node attributions of the network so as to realise the community discovery of the network. This proposed algorithm has low time complexity and high detection accuracy in low coverage networks.