一种面向符号社交网络的负链路预测方法  

A negative link prediction method for signed social networks

作　　者：王伟[1] 薛苗苗 刘沫萌 WANG Wei;XUE Miaomiao;LIU Momeng(School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China)

机构地区：[1]西安工程大学计算机科学学院,陕西西安710048

出　　处：《西安工程大学学报》2021年第3期100-106,共7页Journal of Xi’an Polytechnic University

基　　金：国家自然科学基金资助项目(1902303);陕西省自然科学基础研究计划资助项目(2020JQ-832)。

摘　　要：针对符号社交网络中存在负链路特征融合和挖掘有效信息不足,导致预测性能较低的问题,提出了一种新的特征融合的负链路预测方法。该方法在经典的结构平衡理论和社会地位理论基础上,构造出4种与负符号相关的特征,包括节点特征、结构特征、相似性特征和评分特征,利用逻辑回归算法实现了负链路预测。在Epinions和Slashdot 2个典型的符号网络数据集上对其有效性进行验证。实验结果表明:与基准方法相比,提取的方法的精确率在2个数据集上分别提高了约4.5%和10.4%,F 1分数分别提高了约27.3%和31.5%,达到了提升负链路预测效果的目的。Aiming at the disadvantages of negative link feature fusion and effective information mining in signed social networks,resulting in low prediction performance,a new feature fusion negative link prediction method is proposed.Based on the classic structural balance theory and social status theory,this method constructed four features related to negative signs,including node feature,structural feature,similarity feature,and scoring feature,and used logistic regression algorithms to realize negative links prediction.Its effectiveness was verified on Epinions and Slashdot two typical signed network data sets.Experimental results show that compared with the benchmark method,the accuracy of this method is increased by about 4.5%and 10.4%on the two data sets,respectively,and the F 1 score is increased by about 27.3%and 31.5%,which achieve the goal of improving the effect of negative link prediction.

关 键 词：符号网络 社会网络 链路预测 负面关系 特征融合 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]