Motivated by potential financial gain, companies may hire fraudster groups to
write fake reviews to either demote competitors or promote their own
businesses. Such groups are considerably more successful in misleading
customers, as people are more likely to be influenced by the opinion of a large
group. To detect such groups, a common model is to represent fraudster groups'
static networks, consequently overlooking the longitudinal behavior of a
reviewer thus the dynamics of co-review relations among reviewers in a group.
Hence, these approaches are incapable of excluding outlier reviewers, which are
fraudsters intentionally camouflaging themselves in a group and genuine
reviewers happen to co-review in fraudster groups. To address this issue, in
this work, we propose to first capitalize on the effectiveness of the HIN-RNN
in both reviewers' representation learning while capturing the collaboration
between reviewers, we first utilize the HIN-RNN to model the co-review
relations of reviewers in a group in a fixed time window of 28 days. We refer
to this as spatial relation learning representation to signify the
generalisability of this work to other networked scenarios. Then we use an RNN
on the spatial relations to predict the spatio-temporal relations of reviewers
in the group. In the third step, a Graph Convolution Network (GCN) refines the
reviewers' vector representations using these predicted relations. These
refined representations are then used to remove outlier reviewers. The average
of the remaining reviewers' representation is then fed to a simple fully
connected layer to predict if the group is a fraudster group or not. Exhaustive
experiments of the proposed approach showed a 5% (4%), 12% (5%), 12% (5%)
improvement over three of the most recent approaches on precision, recall, and
F1-value over the Yelp (Amazon) dataset, respectively.

本文提出了一种使用 HIN-RNN 和 Graph Convolution Network 的方法，以解决在静态网络中排除异常点评论者的问题，并在 Yelp 和 Amazon 数据集上将其与其他最近的方法进行了比较实验，结果证明这种方法具有更高的精度、召回率和 F1 值。