差分化节点特征对复杂网络链接预测的分类性能分析  

Classification performance analysis on link prediction via diverse node features in complex network

作　　者：伍杰华[1,2] 朱岸青[1,3] 蔡雪莲[1] 张小兰[1] 

机构地区：[1]广东工贸职业技术学院计算机工程系,广东广州510510 [2]华南理工大学信息科学与技术学院,广东广州510641 [3]暨南大学信息科学与技术学院,广东广州510632

出　　处：《计算机工程与科学》2015年第1期173-178,共6页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(61003045);广东省教育部产学研结合项目(2012B091100043)

摘　　要：链接预测属于复杂网络分析的研究分支,它根据网络历史结构信息预测未来节点间会产生链接的可能性,从而挖掘网络的传播和演化方式。通过引入差分化节点的贡献权重并结合经典的节点和共邻节点网络拓扑结构特征,分别应用七类有监督学习-分类模型对社交、生物、交通等不同领域的八个真实复杂网络数据集进行实验,并采用Precision和ROC曲线对实验结果进行分析与评价。实验表明,引入基于差分化节点的贡献特征能够在深入挖掘网络结构信息的基础上比其余特征有更优的预测精确度,同时差异化的分类模型和特征选择对链接预测性能有相异的影响。Link prediction is a branch of complex network analysis.It predicts future linkage between nodes according to historical network structure information in order to reveal the network evolution and diffusion.This paper introduces a novel feature based on differentiated node contribution and provides seven supervised learning frameworks to carry the classification task via our introduced features and two categories of classic topological features which include node information and common neighbors＇ property.The experimental results on eight real-world networks demonstrate interesting finding on the prediction influence by different classification models and topological diverse features via precision and ROC curve.In addition,we prove that our newly introduced features outperform the classic ones,which can mine the latent network information and enhance accuracy of link prediction.

关 键 词：链接预测 复杂网络 特征选择 分类 共邻节点 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]