Abstract: In order to explore the topological changes of Normal Aging human Brain Functional Network (NABFN), a network evolution Model based on Naive Bayes (NBM) was proposed. Firstly, the probability of existing edges between nodes was defined based on link prediction algorithm of Naive Bayes (NB) and anatomical distance. Secondly, based on the brain functional networks of young people, a specific network evolution algorithm was used to obtain a simulation network of the corresponding middle-aged and old-aged gradually by constantly adding edges. Finally, a network Similarity Index (SI) was proposed to evaluate the similarity degree between the simulation network and the real network. In the comparison experiments with network evolution Model based on Common Neighbor (CNM), the SI values between the simulation networks constructed by NBM and the real networks (4.479 4, 3.402 1) are higher than those of CNM (4.100 4, 3.013 2). Moreover, the SI value of both simulation networks are significantly higher than those of simulation networks derived from random network evolution algorithm (1.892 0, 1.591 2). The experimental results confirm that NBM can predict the topological changing process of NABFN more accurately.

Key words: brain functional network, evolution model, evolution algorithm, link prediction, Naive Bayes (NB)

摘要： 为了对正常衰老的人脑功能网络（NABFN）的拓扑结构变化进行探究，提出一种基于朴素贝叶斯的网络演化模型（NBM）。首先，依据朴素贝叶斯（NB）的链路预测算法与解剖距离来定义节点间存在连边的概率；其次，利用特定的网络演化算法，在青年人的脑功能网络基础上，通过不断地增加连边来逐步得到相应中年及老年时期的模拟网络；最后，为了对模拟网络与真实网络间的相似程度进行评价，提出网络相似指标（SI）值。仿真实验结果表明，与基于共同邻居的网络演化模型（CNM）相比，NBM构建的模拟网络与真实网络间的SI值（4.479 4，3.402 1）高于CNM模拟网络对应的SI值（4.100 4，3.013 2）；并且，两者模拟网络的SI值均明显高于随机网络演化算法所得模拟网络的SI值（1.892 0，1.591 2）。实验结果证实NBM能够更为准确地预测出NABFN的拓扑结构变化过程。

关键词: 脑功能网络, 演化模型, 演化算法, 链路预测, 朴素贝叶斯