基于图的欺诈检测的自主链式思维蒸馏
Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection
AI总结：
FraudCoT通过自主链式思维推理和高效联合训练策略，提升基于图的欺诈检测性能和效率。
摘要：
在带有文本属性的图（TAGs）上进行基于图的欺诈检测需要联合建模丰富的文本语义和关系依赖。然而，现有的LLM增强型GNN方法受到预定义提示和解耦训练流程的限制，限制了推理的自主性并削弱了语义-结构对齐。我们提出了FraudCoT，一个统一的框架，通过自主、图感知的链式思维（CoT）推理和可扩展的LLM-GNN联合训练，推进基于TAG的欺诈检测。为了解决预定义提示的限制，我们引入了一种欺诈感知的选性CoT蒸馏机制，生成多样化的推理路径并增强语义-结构理解。这些蒸馏的CoTs被整合到节点文本中，为GNN提供丰富的、多跳语义和结构线索用于欺诈检测。此外，我们开发了一种高效的非对称联合训练策略，使端到端优化成为可能，同时显著减少了朴素联合训练的计算成本。在公开和工业基准上的广泛实验表明，FraudCoT在最先进的方法上实现了高达8.8%的AUPRC提升，并在训练吞吐量上实现了高达1,066倍的加速，大幅推进了检测性能和效率。
英文摘要：
Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies. However, existing LLM-enhanced GNN approaches are constrained by predefined prompting and decoupled training pipelines, limiting reasoning autonomy and weakening semantic-structural alignment. We propose FraudCoT, a unified framework that advances TAG-based fraud detection through autonomous, graph-aware chain-of-thought (CoT) reasoning and scalable LLM-GNN co-training. To address the limitations of predefined prompts, we introduce a fraud-aware selective CoT distillation mechanism that generates diverse reasoning paths and enhances semantic-structural understanding. These distilled CoTs are integrated into node texts, providing GNNs with enriched, multi-hop semantic and structural cues for fraud detection. Furthermore, we develop an efficient asymmetric co-training strategy that enables end-to-end optimization while significantly reducing the computational cost of naive joint training. Extensive experiments on public and industrial benchmarks demonstrate that FraudCoT achieves up to 8.8% AUPRC improvement over state-of-the-art methods and delivers up to 1,066x speedup in training throughput, substantially advancing both detection performance and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
