节点属性和拓扑信息相结合的脑网络聚类模型

目前，脑网络分类是研究热点，研究者采用不同的方法从标签数据中提取并选择特征，以实现对数据的自动分类，但是从大量的标签数据中提取和选择最优的特征很费时。针对以上问题，提出一种脑网络相似度计算方法并构建基于无偏脑网络的聚类模型。首先，使用余弦相似度和子网络核来度量脑网络的属性相似度和结构相似度，然后将结构相似度和属性相似度集成为一个相似度矩阵，最后利用谱聚类实现脑网络聚类。对openfMRI数据库中的50名精神分裂症患者与49名正常对照组进行了聚类测试，结果显示，Rand指数为0.91，精确率为0.86，召回率为0.98，F1为0.92。研究表明提出的模型能较准确地计算脑网络相似性，表现出较高聚类性能。

A clustering model of brain network basedon node attribute and topology information

At present, brain network classification has become a research focus. Researchers use different methods to extract and select features from label data, in order to realize automatic classification of data. However, it is time consuming to extract and select the optimal features from a large number of label data. In order to solve the above problems, a similarity calculation method of brain network is proposed and a clustering framework based on brain network is constructed. The cosine similarity and the sub network kernel are used to measure the attribute similarity and structural similarity of the brain network, and the structural similarity and attribute similarity are integrated into a similarity matrix. Finally, the spectral clustering is used to realize the clustering of the brain network. A cluster test is carried out on 50 patients with schizophrenia and 49 normal controls in open fMRI database. The results show that the Rand index is 0.91, the accuracy rate is 0.86, the recall rate is 0.98, and the F1 value is 0.92. Therefore, the proposed method can accurately calculate the similarity of brain network and show high clustering performance.