考虑时间特征的电子商务水军群组发现算法.

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 vulnerahle 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. [ABSTRACT FROM AUTHOR]