Bimodal accuracy distribution of link prediction in complex networks

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.

The Absence of a Weak-Tie Effect When Predicting Large-Weight Links in Complex Networks

“…Therefore, they proposed an prediction algorithm, called RA-LP, to address this issue. This algorithm not only improves the accuracy of the edges with high edge betweenness, but it also improves the overall performance [ 42 ]. Shabaz et al proposeed a method to predict future diseases based on existing health status using link prediction, and this explores how long the link survives [ 43 ].…”