An empirical study of a chinese online social network-Renren

Jianwei Niu; Jing Peng; Lei Shu; Chao Tong; Wanjiun Liao

doi:10.1109/MC.2013.1

An empirical study of a chinese online social network-Renren

Jianwei Niu
, Jing Peng
, Lei Shu
, Chao Tong
, Wanjiun Liao

School of Computer Science and Engineering

Research output: Contribution to specialist publication › Article

10 Scopus citations

Abstract

Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.

Original language	English
Pages	78-84
Number of pages	7
Volume	46
No	9
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2013.1
State	Published - 2013

Keywords

Renren
data mining
graph mining
network evolution
network structure
social networks

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10.1109/MC.2013.1

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title = "An empirical study of a chinese online social network-Renren",

abstract = "Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.",

keywords = "Renren, data mining, graph mining, network evolution, network structure, social networks",

author = "Jianwei Niu and Jing Peng and Lei Shu and Chao Tong and Wanjiun Liao",

year = "2013",

doi = "10.1109/MC.2013.1",

language = "英语",

volume = "46",

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AU - Niu, Jianwei

AU - Peng, Jing

AU - Shu, Lei

AU - Tong, Chao

AU - Liao, Wanjiun

PY - 2013

Y1 - 2013

N2 - Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.

AB - Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.

KW - Renren

KW - data mining

KW - graph mining

KW - network evolution

KW - network structure

KW - social networks

UR - https://www.scopus.com/pages/publications/84884390922

U2 - 10.1109/MC.2013.1

DO - 10.1109/MC.2013.1

M3 - 文章

AN - SCOPUS:84884390922

SN - 0018-9162

VL - 46

SP - 78

EP - 84

JO - Computer

JF - Computer

ER -

An empirical study of a chinese online social network-Renren

Abstract

Keywords

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