DFraud3- Multi-Component Fraud Detection freeof Cold-start

TL;DR

DFraud3 introduces a multi-component fraud detection method using Heterogeneous Information Networks and graph induction, effectively addressing cold-start issues and improving accuracy over existing approaches in detecting fraud reviews, users, and items.

Contribution

This paper presents a novel multi-component fraud detection framework leveraging HIN and graph induction, overcoming limitations of prior single-task models and handling cold-start challenges.

Findings

Achieves 13% higher accuracy than state-of-the-art on Yelp.

Effectively detects fraud reviews, users, and items simultaneously.

Addresses cold-start and camouflage issues in fraud detection.

Abstract

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.