用软提示连接大模型与图神经网络，无需文本也能精准识骗。
Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection
- 用软提示桥接图结构与语义空间，摆脱对文本的依赖。
- 引入并行GNN编码器，将多关系拓扑转为细粒度图令牌。
- 端到端优化提升语义对齐，可解释性更强，适合反欺诈场景。
近年来，大语言模型（LLM）在处理图任务如欺诈检测方面展现出强大能力。然而，现有方法严重依赖丰富的文本属性，而该领域常缺乏文本数据。尽管部分开创性方法尝试通过硬提示将图结构文本化，但易导致特征失真。此外，欺诈检测具有多关系复杂性，现有方法难以捕捉深层语义信息。为此，我们提出大模型-图神经网络软提示框架（LGSPF）。具体而言，LGSPF利用软提示连接图结构与语义空间，消除对文本的依赖；进一步引入并行图神经网络（GNN）编码器，将多关系拓扑转化为图令牌，实现细粒度的LLM欺诈理解。通过端到端优化，LGSPF增强LLM与GNN间的深层语义对齐。在多个欺诈检测基准上的实验表明，本方法达到当前最优性能。此外，我们还验证了LGSPF在提升欺诈行为语义可解释性方面的贡献。
原文摘要 · Abstract (English)
In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing methods rely heavily on rich text attributes, which poses difficulties for this domain due to the lack of textual data. Although some pioneering methods attempt to overcome it, their textualization of graph structures via hard prompts easily leads to feature distortion. Additionally, fraud detection often exhibits multi-relational complexity, where current methods struggle to capture this deep semantic information. To address these challenges, we propose LLM-GNN Soft Prompt Framework (LGSPF). Specifically, LGSPF bridges the graph structure and semantic space using soft prompt to eliminate reliance on text. We further introduce a parallel Graph Neural Network (GNN) encoder to translate multi-relational topologies into graph tokens for fine-grained LLM fraud comprehension. Through end-to-end optimization, LGSPF enhances deep semantic alignment between LLM and GNN. Experiments across diverse fraud detection benchmarks demonstrate our method achieves state-of-the-art performance. Moreover, we further validate the contribution of LGSPF on enhancing the semantic interpretability of fraud behaviors.
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