TY - GEN
T1 - Toward interests drift mechanism for social network
AU - Zhang, Yutao
AU - Liu, Gongshen
AU - Wu, Jun
AU - Li, Jianhua
AU - Guo, Longhua
N1 - Funding Information:
This work is supported by National Key Basic Research Program of China (Grant NO: 2013CB329603), the National Natural Science Foundation of China with Grant No. 61472248, 61431008 and 61401273.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/19
Y1 - 2016/9/19
N2 - In the real world, there are many complex networks with a certain number of nodes. Complex network theory has been used in many fields such as World Trade Web. As an important part of complex network, several social network models have been proposed to simulate the social networks in real world in the last decades. While there are plenty of characteristics and current models cannot describe them. In this paper, a new network model is proposed, whose name is the Interests Drift Network Model (IDNM). A network closed to real-life can be created by IDNM, because the formation of communities is based on nodes' various preference of fields of interest, and each node's interest fields may change every once in a while. In the experiments, we simulate the growing process of IDNM and measure some characteristics. It's shown that there are differences between IDNM and traditional network model: the parameters such as modularity and degree diversion are more similar to real-life networks.
AB - In the real world, there are many complex networks with a certain number of nodes. Complex network theory has been used in many fields such as World Trade Web. As an important part of complex network, several social network models have been proposed to simulate the social networks in real world in the last decades. While there are plenty of characteristics and current models cannot describe them. In this paper, a new network model is proposed, whose name is the Interests Drift Network Model (IDNM). A network closed to real-life can be created by IDNM, because the formation of communities is based on nodes' various preference of fields of interest, and each node's interest fields may change every once in a while. In the experiments, we simulate the growing process of IDNM and measure some characteristics. It's shown that there are differences between IDNM and traditional network model: the parameters such as modularity and degree diversion are more similar to real-life networks.
KW - communities
KW - interests drift
KW - model
KW - social networks
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U2 - 10.1109/SSIC.2016.7571804
DO - 10.1109/SSIC.2016.7571804
M3 - Conference contribution
AN - SCOPUS:84992053212
T3 - 2016 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications, SSIC 2016 - Proceedings
BT - 2016 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications, SSIC 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications, SSIC 2016
Y2 - 18 July 2016 through 19 July 2016
ER -