中国邮电高校学报(英文) ›› 2014, Vol. 21 ›› Issue (4): 54-63.doi: 10.1016/S1005-8885(14)60316-2

• Networks • 上一篇    下一篇

Online social network model with renewal and accelerated growth

吴哲,郭宇春,陈常嘉   

  1. School of Electrical and Computer Engineering, Beijing Jiaotong University, Beijing 100044, China
  • 收稿日期:2014-03-06 修回日期:2014-06-15 出版日期:2014-08-31 发布日期:2014-08-30
  • 通讯作者: 吴哲 E-mail:zhewoo@gmail.com
  • 基金资助:

    国家自然科学基金;北京交通大学基础研究基金

Online social network model with renewal and accelerated growth

  1. School of Electrical and Computer Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2014-03-06 Revised:2014-06-15 Online:2014-08-31 Published:2014-08-30
  • Contact: Zhe Wu E-mail:zhewoo@gmail.com
  • Supported by:

    National Natural Science Foundation of China;the Fundamental Research Funds in Beijing Jiaotong University

摘要: Based on observation of the growing mechanism in Twitter-like online social networks, an online social network (OSN) evolution model was proposed. a renewal mechanism for the old nodes and an accelerated growth mechanism was introduced for the new nodes, comparing with the native copying model. Topological characteristics of the generated networks, such as degree distribution, average shortest-path length and clustering coefficient, are analyzed and numerized. These properties are validated with some crawled datasets of real online social networks.

关键词:

online social network, network evolution model, degree distribution, average shortest-path length, clustering coefficient

Abstract: Based on observation of the growing mechanism in Twitter-like online social networks, an online social network (OSN) evolution model was proposed. a renewal mechanism for the old nodes and an accelerated growth mechanism was introduced for the new nodes, comparing with the native copying model. Topological characteristics of the generated networks, such as degree distribution, average shortest-path length and clustering coefficient, are analyzed and numerized. These properties are validated with some crawled datasets of real online social networks.

Key words:

online social network, network evolution model, degree distribution, average shortest-path length, clustering coefficient