Social Network Analysis Based on Network Motifs
Author(s) -
Honglin Xu,
Yan Han-bing,
Cuifang Gao,
Zhu Ping
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/874708
Subject(s) - centrality , social network analysis , computer science , social network (sociolinguistics) , function (biology) , data mining , community structure , network analysis , theoretical computer science , mathematics , statistics , world wide web , social media , physics , quantum mechanics , evolutionary biology , biology
Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. In application analysis, our approach is shown to be effective
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