Tairan Huang , Qiutong Li , Cong Xu , Jianliang Gao , Zhao Li , Shichao Zhang
{"title":"Revisiting low-homophily for graph-based fraud detection","authors":"Tairan Huang , Qiutong Li , Cong Xu , Jianliang Gao , Zhao Li , Shichao Zhang","doi":"10.1016/j.neunet.2025.107407","DOIUrl":null,"url":null,"abstract":"<div><div>The openness of Internet stimulates a large number of fraud behaviors which have become a huge threat. Graph-based fraud detectors have attracted extensive interest since the abundant structure information of graph data has proved effective. Conventional Graph Neural Network (GNN) approaches reveal fraudsters based on the homophily assumption. But fraudsters typically generate heterophilous connections and label-imbalanced neighborhood. Such behaviors deteriorate the performance of GNNs in fraud detection tasks due to the low homophily in graphs. Though some recent works have noticed the challenges, they either treat the heterophilous connections as homophilous ones or tend to reduce heterophily, which roughly ignore the benefits from heterophily. In this work, an integrated two-strategy framework HeteGAD is proposed to balance both homophily and heterophily information from neighbors. The key lies in explicitly shrinking intra-class distance and increasing inter-class segregation. Specifically, the Heterophily-aware Aggregation Strategy tease out the feature disparity on heterophilous neighbors and augment the disparity between representations with different labels. And the Homophily-aware Aggregation Strategy are devised to capture the homophilous information in global text and augment the representation similarity with the same label. Finally, two corresponding inter-relational attention mechanisms are incorporated to refine the procedure of modeling the interaction of multiple relations. Experiments are conducted to evaluate the proposed method with two real-world datasets, and demonstrate that the HeteGAD outperforms 11 state-of-the-art baselines for fraud detection.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107407"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025002862","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The openness of Internet stimulates a large number of fraud behaviors which have become a huge threat. Graph-based fraud detectors have attracted extensive interest since the abundant structure information of graph data has proved effective. Conventional Graph Neural Network (GNN) approaches reveal fraudsters based on the homophily assumption. But fraudsters typically generate heterophilous connections and label-imbalanced neighborhood. Such behaviors deteriorate the performance of GNNs in fraud detection tasks due to the low homophily in graphs. Though some recent works have noticed the challenges, they either treat the heterophilous connections as homophilous ones or tend to reduce heterophily, which roughly ignore the benefits from heterophily. In this work, an integrated two-strategy framework HeteGAD is proposed to balance both homophily and heterophily information from neighbors. The key lies in explicitly shrinking intra-class distance and increasing inter-class segregation. Specifically, the Heterophily-aware Aggregation Strategy tease out the feature disparity on heterophilous neighbors and augment the disparity between representations with different labels. And the Homophily-aware Aggregation Strategy are devised to capture the homophilous information in global text and augment the representation similarity with the same label. Finally, two corresponding inter-relational attention mechanisms are incorporated to refine the procedure of modeling the interaction of multiple relations. Experiments are conducted to evaluate the proposed method with two real-world datasets, and demonstrate that the HeteGAD outperforms 11 state-of-the-art baselines for fraud detection.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.