摘要
针对在电子商务平台上普遍存在的网络水军,提出了一个综合考虑网络结构与时间特征的算法来检测评论网络中的水军群组。该算法由四步组成:a)基于评论网络结构特征的分析挖掘出易受水军攻击的目标产品;b)受“共爆发现象”的启发,提出了一个目标产品被水军群组攻击的可疑时期挖掘算法;c)基于目标产品可疑时期内的数据,构造目标产品—评论者的诱导子图,并在该子图上应用层次凝聚聚类算法生成候选水军群组;d)为了过滤掉在可疑时期内购物并评论的正常用户,提出了一个水军群组净化方法,然后基于评论者的行为特征对净化后的群组进行分类。基于真实数据集的实验结果表明,该算法可以准确、高效地检测活跃在电子商务网站上的网络水军群组。
Aiming at the ubiquitous network spammers on e-commerce platform,this paper proposed an algorithm considering network structure and time characteristics to detect the spammer groups in the comment network.The algorithm consisted of four steps:a)mining the target products that were vulnerable to attack by the spammers based on the analysis of the structural characteristics of the comment network;b)this paper proposed an algorithm for mining the suspicious period when the spammer groups attacked the target product inspired by the“co-bursting phenomenon”;c)this paper constructed the induced subgraph of target products-reviewers based on the data of target product in suspicious period,and applied hierarchical agglomerative clustering algorithm to generate candidate spammer groups on the subgraph;d)in order to filter out the normal users who shopped and commented during the suspicious period,this paper proposed a spammer groups purification method,and then classified the purified groups based on the behavior characteristics of the reviewers.The experimental results based on real data sets show that the proposed algorithm can accurately and efficiently detect the network spammer groups active on e-commerce websites.
作者
张文鹏
纪淑娟
李金鹏
张琪
Zhang Wenpeng;Ji Shujuan;Li Jinpeng;Zhang Qi(Shandong Provincial Key Laboratory of Wisdom Mine Information Technology,Shandong University of Science&Technology,Qingdao Shandong 266590,China)
出处
《计算机应用研究》
CSCD
北大核心
2021年第8期2321-2327,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(71772107,62072288)。
关键词
电子商务
水军群组
可疑时期
层次聚类
electronic commerce
spammer groups
suspicious period
hierarchical clustering
