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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called\n            <jats:italic>Graph-Oriented Snorkel<\/jats:italic>\n            (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the\n            <jats:italic>Multiple Edge-Types Based Attention<\/jats:italic>\n            mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.\n          <\/jats:p>","DOI":"10.1145\/3623401","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T12:26:52Z","timestamp":1695299212000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan Services"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6814-3456","authenticated-orcid":false,"given":"Hao","family":"Tang","sequence":"first","affiliation":[{"name":"Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-0316","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Tongji University, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai Artificial Intelligence Laboratory, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2264-0987","authenticated-orcid":false,"given":"Jianguo","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0637-9317","authenticated-orcid":false,"given":"Changjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tongji University, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai Artificial Intelligence Laboratory, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1145\/3292500.3330693","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Babaev Dmitrii","year":"2019","unstructured":"Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin, and Dmitrii Umerenkov. 2019. 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