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Graph-based fraud detection has gained significant attention in recent years, reflecting its growing potential to mitigate sophisticated fraudulent activities. The main objective of graph-based fraud detection is to distinguish between fraudsters and normal entities within graphs. While real-world networks contain complex, high-order relationships, existing graph-based fraud detection methods focus solely on pairwise interactions, overlooking non-pairwise relationships and the broader dependencies among entities within fraud graphs. Thus, we highlight the importance of exploring non-pairwise relationships to build a more effective fraud detection model. In this paper, we propose TROPICAL, a novel <jats:underline>TR<\/jats:underline>ansf<jats:underline>O<\/jats:underline>rmer-based hy<jats:underline>P<\/jats:underline>ergraph Learn<jats:underline>I<\/jats:underline>ng framework for detecting <jats:underline>CA<\/jats:underline>mouflaged ma<jats:underline>L<\/jats:underline>icious actors in online social networks. To capture comprehensive high-order relations, we construct a hypergraph from the original input graph. However, constructing the hypergraph can be computationally intensive. TROPICAL addresses this challenge by carefully selecting moderate hyperparameters, creating a balance between computational efficiency and effectively capturing high-order relationships. TROPICAL learns node representations by processing multiple hyperedge groups and incorporates positional encodings into the aggregated information to enhance their distinctiveness. The aggregated sequential information is then passed through a transformer encoder, enabling the model to generate rich, high-order representations to detect camouflaged fraudsters. Extensive experiments on two real-world datasets demonstrate TROPICAL\u2019s superior performance compared to the state-of-the-art fraud detection models. The source codes and the datasets of our work are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/VenusHaghighi\/TROPICAL\" ext-link-type=\"uri\">https:\/\/github.com\/VenusHaghighi\/TROPICAL<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s10115-025-02476-5","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T09:17:15Z","timestamp":1748423835000},"page":"7987-8022","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Beyond pairwise relationships: a transformer-based hypergraph learning approach for fraud detection"],"prefix":"10.1007","volume":"67","author":[{"given":"Venus","family":"Haghighi","sequence":"first","affiliation":[]},{"given":"Behnaz","family":"Soltani","sequence":"additional","affiliation":[]},{"given":"Nasrin","family":"Shabani","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"key":"2476_CR1","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2015","unstructured":"Akoglu L, Tong H, Koutra D (2015) Graph based Anomaly Detection and Description: A Survey. 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Venus Haghighi&apos;s work was also partially supported by a Digital Finance CRC Top-Up Scholarship and her conference attendanceCooperation was partially supported by a Google Travel Grant.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}