The proliferation of digital platforms has enabled fraudsters to deploy sophisticated camouflage techniques, such as multi-hop collaborative attacks, to evade detection. Traditional Graph Neural Networks (GNNs) often fail to capture these complex high-order patterns due to limitations including homophily assumption failures, severe label imbalance, and noise amplification during deep aggregation. To address these challenges, we propose the Hypergraph-based Contrastive Learning Network (HCLNet), a novel framework integrating three synergistic innovations. Firstly, multi-relational hypergraph fusion encodes heterogeneous associations into hyperedges, explicitly modeling group-wise fraud syndicates beyond pairwise connections. Secondly, a multi-head gated hypergraph aggregation mechanism employs parallel attention heads to capture diverse fraud patterns, dynamically balances original and high-order features via gating, and stabilizes training through residual connections with layer normalization. Thirdly, hierarchical dual-view contrastive learning jointly applies feature masking and topology dropout at both node and hyperedge levels, constructing augmented views to optimize self-supervised discrimination under label scarcity. Extensive experiments on two real-world datasets demonstrate HCLNet's superior performance, achieving significant improvements over the baselines across key evaluation metrics. The model's ability to reveal distinctive separation patterns between fraudulent and benign entities underscores its practical value in combating evolving camouflaged fraud tactics in digital ecosystems.
Wang et al. (Wed,) studied this question.