Articles written in Pramana – Journal of Physics
Volume 82 Issue 3 March 2014 pp 571-583 Research Articles
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
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.
Volume 88 Issue 3 March 2017 Article ID 0044 Research Article
Refinement of the community detection performance by weighted relationship coupling
The complexity of many community detection algorithms is usually an exponential function with the scale which hard to uncover community structure with high speed. Inspired by the ideas of the famous modularity optimization, in this paper, we proposed a proper weighting scheme utilizing a novel k-strength relationship whichnaturally represents the coupling distance between two nodes. Community structure detection using a generalized weighted modularity measure is refined based on the weighted k-strength matrix. We apply our algorithm on both the famous benchmark network and the real networks. Theoretical analysis and experiments show that the weighted algorithm can uncover communities fast and accurately and can be easily extended to large-scale real networks.
Volume 97, 2023
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