文献类型：期刊文献

中文题名：Modeling online social networks based on preferential linking

英文题名：Modeling online social networks based on preferential linking

作者：Hu Hai-Bo[1];Guo Jin-Li[2];Chen Jun[1]

机构：[1]E China Univ Sci & Technol, Dept Management, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Management, Shanghai 200093, Peoples R China

年份：2012

卷号：21

期号：11

中文期刊名：Chinese Physics B

外文期刊名：CHINESE PHYSICS B

收录：CSTPCD;;EI(收录号：20124715690950);Scopus;WOS:【SSCI(收录号:WOS:000310950400090)，SCI-EXPANDED(收录号:WOS:000310950400090)】；CSCD:【CSCD2011_2012】；

基金：Project supported by the National Natural Science Foundation of China (Grant Nos. 61104139, 70871082, and 71101053) and the ECUST for Excellent Young Scientists, China.

语种：英文

中文关键词：online social network;preferential linking;model;power law

外文关键词：online social network; preferential linking; model; power law

摘要：We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro=mechanisms of network growth and the macrostructures of online social networks.
We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks.