IN-GFD: An Interpretable Graph Fraud Detection Model for Spam Reviews

With the development of the e-commerce platform, more and more reviews of its various formats continue to appear. Reviews help people buy the right item faster, and instead, spam reviews reduce the user experience. To be able to detect spam reviews, statistical machine learning-based methods were commonly used in the past, but these approaches ignored the correlation between reviews. With the development of the graph fraud detection model, people have started to graph model the review data. However, typical graph fraud detection models still have problems with interpretability. Therefore, we propose here an interpretable graph fraud detection model for spam reviews, which is also named IN-GFD. As for the interpretability issue, we leveraged the relationship against the predicted score and whether a review is spam or not to build a loss function on top of the feature-embedding matrix, and introduced a scoring difference threshold mechanism, which can allow our model to have antehoc interpretability. In addition, to address class imbalance issues, IN-GFD utilizes the oversampling of the spam nodes to balance them with normal nodes and introduces an edge-loss function to learn new edge relationships. After extensive experiments, our method proves to be better than other state-of-the-arts (SOTA) models in terms of fraud detection and offers the benefit of interpretability. Finally, our study combines detection models with antehoc interpretability, offering a promising direction in review detection. Our approach has wide applicability, detecting spam reviews in datasets with user reviews and providing reasonable interpretations.