{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rlc-gnn-an-improved-deep-architecture-for","title":"RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection","arxiv_id":null,"date":"2021-06-18","proceeding":"Applied Sciences 2021 6","authors":["Yufan Zeng","Jiashan Tang"],"abstract":"Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.","url_abs":"https://www.mdpi.com/2076-3417/11/12/5656/htm","url_pdf":"https://www.mdpi.com/2076-3417/11/12/5656/htm","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fraud-detection-on-amazon-fraud","task":"Fraud Detection","dataset":"Amazon-Fraud","model":"RLC-GNN","rank_in_archive_order":3,"of":6,"metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false},{"leaderboard":"/sota/fraud-detection-on-yelp-fraud","task":"Fraud Detection","dataset":"Yelp-Fraud","model":"RLC-GNN","rank_in_archive_order":7,"of":10,"metrics":{"AUC-ROC":"85.44"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-amazon-fraud","task":"Node Classification","dataset":"Amazon-Fraud","model":"RLC-GNN","rank_in_archive_order":3,"of":6,"metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-yelpchi","task":"Node Classification","dataset":"Yelp-Fraud","model":"RLC-GNN","rank_in_archive_order":6,"of":9,"metrics":{"AUC-ROC":"85.44"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}