FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation

Key points

• Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk label.1
• An intuitive approach is to adapt existing graph-enhanced large language models (LLMs), which inject graph structure into LLMs via graph-conditioned instruction tuning or structure-aware embeddings.1
• To evaluate explanation quality, we introduce a task-specific rubric that assesses faithfulness, graph grounding, helpfulness, consistency, and conciseness, and validate it against human judgments.1
• To train our graph agent, we propose a rubric-guided trajectory distillation strategy through which the agent learns both tool-use behavior and structured explanation generation.1

Sources (1)

• [1]FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:16 AM

  In this paper, we propose FRaud Explanation Generation via Agentic Graph Context NavigaTION (FRACTION), a framework for graph fraud explanation generation.

  Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk label.

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