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Predicting the growth of new links by new preferential attachment similarity indices |
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| Authors: | KE HU JU XIANG XIAO-KE XU HUI-JIA LI WAN-CHUN YANG YI TANG |
| Institution: | 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 |
| Abstract: | 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. |
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