Link Prediction Algorithm Based on Density Peak Clustering
SHAO Hao1， WANG Lunwen1， DENG Jian2
1（College of Electronic Engineering， National University of Defense Technology，Hefei 230031，China）2（Shijiazhuang Campus，Army Engineering University of PLA，Shijiazhuang 050003，China）
Abstract：Traditional link prediction algorithms based on network structure only consider the single similarity index The prediction results in different networks have obvious differences and the prediction accuracy is low.To solve this problem， this paper considered the complementarity of different indexes， and proposed a link prediction algorithm based on adaptive fusion of multiple indexes.Firstly， we proposed two improved path similarity indexes PLD and INR， which consider the contribution of path intermediate links and intermediate nodes to prediction respectively；Secondly， we transformed the problem of whether there were links between nodes into a twoclass problem and combined the above indexes with neighborhood similarity index， random walk index as multidimensional attributes of node pairs；Finally， we classified node pairs by density peak clustering and determined the link properties of each node pair according to the classification results.The simulation results show that the prediction accuracy of proposed algorithm is significantly higher than that of traditional similarity prediction algorithms in various networks.
邵豪,王伦文,邓健. 一种基于密度峰值聚类的链路预测算法[J]. 小型微型计算机系统, 2020, 41(5): 1007-1012.
SHAO Hao,WANG Lunwen,DENG Jian. Link Prediction Algorithm Based on Density Peak Clustering. Journal of Chinese Computer Systems, 2020, 41(5): 1007-1012.
2020, Vol. 41 
