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Bimodal accuracy distribution of link prediction in complex networks

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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)

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.

Suggested Citation

  • Chengjun Zhang & Ming Qian & Xinyu Shen & Qi Li & Yi Lei & Wenbin Yu, 2023. "Bimodal accuracy distribution of link prediction in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(08), pages 1-20, August.
    • Handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:08:n:s0129183123500985
    DOI: 10.1142/S0129183123500985
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