Predicting the growth of new links by new preferential attachment similarity indices
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- 作者：KE HU (1)
JU XIANG (2)
XIAO-KE XU (3)
HUI-JIA LI (4)
WAN-CHUN YANG (5)
YI TANG (1) - 关键词：Link prediction ; preferential attachment ; network evolution ; 89.75.Fb ; 89.20.Ff ; 89.75.Hc
- 刊名：Pramana
- 出版年：2014
- 出版时间：March 2014
- 年：2014
- 卷：82
- 期：3
- 页码：571-583
- 全文大小：374 KB
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JU XIANG (2)
XIAO-KE XU (3)
HUI-JIA LI (4)
WAN-CHUN YANG (5)
YI TANG (1)
1. Hunan Key Laboratory for Micro-Nano Energy Materials and Devices, and Laboratory for Quantum Engineering and Micro-Nano Energy Technology, Xiangtan University, Xiangtan, 411105, China
2. Department of Basic Sciences, The First Aeronautical Institute of the Air Force, Xinyang, 464000, China
3. College of Information and Communication Engineering, Dalian Nationalities University, Dalian, 116605, China
4. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 100080, China
5. College of Information Engineering, Xiangtan University, Xiangtan, 411105, China - ISSN：0973-7111
文摘
By revisiting the preferential attachment (PA) mechanism for generating a classical scale-free network, we propose a class of novel preferential attachment similarity indices for predicting future links in evolving networks. Extensive experiments on 14 real-life networks show that these new indices can provide more accurate prediction than the traditional one. Due to the improved prediction accuracy and low computational complexity, these proposed preferential attachment indices can be helpful for providing both instructions for mining unknown links and new insights to understand the underlying mechanisms that drive the network evolution.