Link Prediction in Brain Networks Based on a Hierarchical Random Graph Model

作     者：Yanli Yang Hao Guo Tian Tian Haifang Li 

作者机构：School of Computer Science and Technology Taiyuan University of Technology 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2015年第20卷第3期

页      面：306-315页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：financially supported by the National Natural Science Foundation of China (Nos. 61170136, 61373101, 61472270, and 61402318) the Natural Science Foundation of Shanxi (No. 2014021022-5) the Special/Youth Foundation of Taiyuan University of Technology (No. 2012L014) Youth Team Fund of Taiyuan University of Technology (Nos. 2013T047 and 2013T048) 

主　　题：brain network link prediction hierarchical random graph maximum likelihood estimation method 

摘      要：Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.