Bimodal accuracy distribution of link prediction in complex networks

Chengjun Zhang, Ming Qian, Xinyu Shen, Qi Li, Yi Lei and Wenbin Yu
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Chengjun Zhang: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Ming Qian: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Xinyu Shen: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Qi Li: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Yi Lei: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Wenbin Yu: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 08, 1-20

Abstract: Link prediction plays an important role in information filtering and numerous research works have been made in this field. However, traditional link prediction algorithms mainly focus on overall prediction accuracy, ignoring the heterogeneity of the prediction accuracy for different links. In this paper, we analyzed the prediction accuracy of each link in networks and found that the prediction accuracy for different links is severely polarized. Further analysis shows that the accuracy of edges with low edge betweenness is consistently high while that of edges with high edge betweenness is consistently low, i.e. AUC follows a bimodal distribution with one peak around 0.5 and the other peak around 1. Our results indicate that link prediction algorithms should focus more on edges with high betweenness instead of edges with low betweenness. To improve the accuracy of edges with high betweenness, we proposed an improved algorithm called RA_LP which takes advantage of resource transfer of the second-order and third-order paths of local path. Results show that this algorithm can improve the link prediction accuracy for edges with high betweenness as well as the overall accuracy.

Keywords: Complex networks; link prediction; edge betweenness; heterogeneity (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123500985

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