Open AccessA Community Discovery Algorithm for Complex Networks
Author(s) -
Lintao Lv,
WU Jia-lin,
Hui Lv
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1533/3/032076
Subject(s) - complex network , computer science , community structure , data mining , feature (linguistics) , dynamic network analysis , network science , similarity (geometry) , process (computing) , complex system , fractal , evolving networks , artificial intelligence , theoretical computer science , machine learning , data science , mathematics , computer network , mathematical analysis , linguistics , philosophy , combinatorics , world wide web , image (mathematics) , operating system
Community structure is an important feature of complex networks. These community structures have the fractal characteristics, that is, there is a self similarity of statistical sense between the complex networks and their local. There have been more and more recent researches on communities’ discovery in complex network. However, most existing approaches require the complete information of entire network, which is impractical for some networks, e.g. the dynamical network and the network that is too large to get the whole information. Therefore, the study of community discovery in complex networks has rather important theoretical and practical value. Through the analysis and study of the complex network evolution models with renormalization and the community change of the complex network evolution, using the tool of adjusting scales as the renormalization process, a multi-scale network community detection algorithm based on fractal feature evolution was proposed. The purpose is to solve community discovery problems in dynamic complex networks, and the effectiveness of the proposed method is verified by real data sets. By comparing result of this paper with the previous methods on some real world networks, and experimental results verify the feasibility and accuracy.