  Fraud review detection is a hot research topic inrecent years. The Cold-start
is a particularly new but significant problem referring to the failure of a
detection system to recognize the authenticity of a new user. State-of-the-art
solutions employ a translational knowledge graph embedding approach (TransE) to
model the interaction of the components of a review system. However, these
approaches suffer from the limitation of TransEin handling N-1 relations and
the narrow scope of a single classification task, i.e., detecting fraudsters
only. In this paper, we model a review system as a Heterogeneous
InformationNetwork (HIN) which enables a unique representation to every
component and performs graph inductive learning on the review data through
aggregating features of nearby nodes. HIN with graph induction helps to address
the camouflage issue (fraudsterswith genuine reviews) which has shown to be
more severe when it is coupled with cold-start, i.e., new fraudsters with
genuine first reviews. In this research, instead of focusing only on one
component, detecting either fraud reviews or fraud users (fraudsters), vector
representations are learnt for each component, enabling multi-component
classification. In other words, we are able to detect fraud reviews,
fraudsters, and fraud-targeted items, thus the name of our approach DFraud3.
DFraud3 demonstrates a significant accuracy increase of 13% over the state of
the art on Yelp.
