复杂网络节点相似性算法及其在癫痫病辅助诊断的应用

Node Similarity Algorithm on Complex Network and Its Application in Epilepsy Auxiary Diagnosis

节点相似性分析是链路预测和社团挖掘中的重要部分.引入CN(Common Neighbor,共同邻居)算法、RA(Resource Allocation,资源分配)算法、AA(Adamic-Adar)算法、Sorenson算法等四种节点相似性算法作用于真实网络以及仿真网络(即小世界网络和无标度网络)网络,计算AUC(Area Under the Curve,曲线下面积)曲线从而比较算法的预测准确性,结果表明RA算法的预测准确性优于其他三种算法.随后将四种算法用于分析8例全身性癫痫患者脑电数据功能连接网络,结果发现RA算法预测准确性最佳,通过RA算法能确定最大节点相似度组成的节点簇,为量化大脑功能状态提供客观指标,未来可以将该方法用于临床辅助诊断.

The investigation of node similarity is an important component in link prediction and community detection. In this paper, four kinds of algorithms including common neighbor（CN）, resource allocation（RA）, Adamic-Adar（AA） and Sorenson are introduced into various kinds of real networks and two kinds of simulation networks comprised of small world network and scale free network. The Area Under the Curve（AUC） is computed to compare their predictive accuracy. It＇s found that RA performs much better than the other three kinds of algorithms. Then four algorithms are adopted in functional connectivity networks that characterize electroencephalograph（EEG） recordings from eight patients with generalized epilepsy. It＇s demonstrated that RA performs best from the point of prediction accuracy. According to RA technique, clusters could be determined from nodes that own maximum similarity which provides an objective index for quantifying brain condition, and this might be applied for clinical auxiliary diagnosis in the future.