Predicting the growth of new links by new preferential attachment similarity indices
Ke Hu Ju Xiang Xiao-Ke Xu Hui-Jia Li Wan-Chun Yang Yi Tang
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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.
Ke Hu1 Ju Xiang2 Xiao-Ke Xu3 Hui-Jia Li4 Wan-Chun Yang5 Yi Tang1
Volume 97, 2023
Continuous Article Publishing mode
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