Due to the popularity of crowdsourcing, more crowds are participating in crowdsourcing tasks. However, the proportion of spam workers is continuously increasing due to the openness of crowdsourcing platforms and their incentive mechanisms. To defend against threats from spam workers, researchers have proposed reputation-based and verification-based detection methods, but they either cannot address various collusion patterns or are costly when facing a large number of spam workers with "good" reputations due to collusion. Therefore, we propose a spam worker detection approach based on heterogeneous network embedding. We first model three collusion patterns and analyze the characteristics of spam workers to provide a theoretical basis for detecting spam workers. We then transform the problem of spam worker detection into a node classification problem in a crowdsourcing heterogeneous network in which the vectors of worker nodes are learned using network embedding. To improve the efficiency of network embedding, we propose an improved variable-length random walk algorithm based on node centrality. Finally, based on the obtained vectors of worker nodes, a one-class SVM is used to detect spam workers. The experiments demonstrate that our proposed approach can effectively detect spam workers in different collusion patterns and that the proposed random walk algorithm can reduce the time spent on model training while improving the efficiency of network embedding.

由于众包的普及，更多的人群正在参与众包任务。但是，由于众包平台的开放性及其激励机制，垃圾邮件工作者的比例正在不断增加。为了抵御垃圾邮件工作者的威胁，研究人员提出了基于信誉和基于验证的检测方法，但是它们要么无法解决各种共谋模式，要么面对着大量因勾结而具有“良好”声誉的垃圾邮件工作者，代价很高。因此，我们提出了一种基于异构网络嵌入的垃圾邮件工作者检测方法。我们首先对三种串谋模式进行建模，并分析垃圾邮件工作者的特征，从而为检测垃圾邮件工作者提供理论依据。然后，我们将垃圾邮件工作者检测问题转化为众包异构网络中的节点分类问题，在该网络中，使用网络嵌入来学习工作者节点的向量。为了提高网络嵌入的效率，我们提出了一种基于节点中心度的改进的变长随机游走算法。最后，基于获得的工作节点矢量，使用一类支持向量机检测垃圾邮件工作人员。实验表明，本文提出的方法可以有效地检测不同共谋模式下的垃圾邮件工作者，并且提出的随机游走算法可以减少模型训练所花费的时间，同时提高网络嵌入效率。为了提高网络嵌入的效率，我们提出了一种基于节点中心度的改进的变长随机游走算法。最后，基于获得的工作节点矢量，使用一类支持向量机检测垃圾邮件工作人员。实验表明，本文提出的方法可以有效地检测不同共谋模式下的垃圾邮件工作者，并且提出的随机游走算法可以减少模型训练所花费的时间，同时提高网络嵌入效率。为了提高网络嵌入的效率，我们提出了一种基于节点中心度的改进的变长随机游走算法。最后，基于获得的工作节点矢量，使用一类支持向量机检测垃圾邮件工作人员。实验表明，本文提出的方法可以有效地检测不同共谋模式下的垃圾邮件工作者，并且提出的随机游走算法可以减少模型训练所花费的时间，同时提高网络嵌入效率。