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selforganizingmapofcomplexnetworksforcommunitydetection
Li Zhenping1; Wang Ruisheng2; Zhang Xiangsun3; Chen Luonan4
2010
Source Publicationjournalofsystemsscienceandcomplexity
ISSN1009-6124
Volume000Issue:005Pages:931
AbstractDetecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map (SOM) based approach to community detection. By adopting a new operation and a new weightupdating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/38236
Collection应用数学研究所
Affiliation1.北京物资学院
2.中国人民大学
3.中国科学院数学与系统科学研究院
4.大阪产业大学
Recommended Citation
GB/T 7714
Li Zhenping,Wang Ruisheng,Zhang Xiangsun,et al. selforganizingmapofcomplexnetworksforcommunitydetection[J]. journalofsystemsscienceandcomplexity,2010,000(005):931.
APA Li Zhenping,Wang Ruisheng,Zhang Xiangsun,&Chen Luonan.(2010).selforganizingmapofcomplexnetworksforcommunitydetection.journalofsystemsscienceandcomplexity,000(005),931.
MLA Li Zhenping,et al."selforganizingmapofcomplexnetworksforcommunitydetection".journalofsystemsscienceandcomplexity 000.005(2010):931.
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