用大模型推理识别动态知识图谱中的早期异常信号
C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning
- 通过大模型链式思考找出与主流叙事冲突的稀有子图
- 在多时间快照中追踪稀有子图持续性，精准定位弱信号
- 适合关注舆情预警、事件预判等早期发现场景
弱信号是重大变化发生前的早期、低可见度征兆。现有方法依赖关键词频率、主题建模或无类型图拓扑，难以捕捉信号所体现的语义与关系结构。本文提出C-Unseen，一种自解释的动态时序知识图谱（DTKG）弱信号检测框架。将弱信号定义为在连续TKG快照中广泛传播的稀有且语义连贯的子图。框架包含两个模块：稀有子图提取器利用大模型通过思维链推理，识别内容与主导快照叙事相悖的子图；弱信号警报器则追踪这些子图在时间维度上的持续性，从而筛选出真实弱信号。实验表明，C-Unseen显著优于基于关键词、主题和图结构的基线方法。
原文摘要 · Abstract (English)
Weak signals are early, low-visibility indicators that precede significant changes before those changes become established. Existing detection methods, based on keyword frequency, topic modeling, or untyped graph topology, fail to capture the semantic and relational structure through which such signals manifest. In this paper, we propose C-Unseen, a self-interpretable framework for weak signal detection in Dynamic Temporal Knowledge Graphs (DTKGs). We define a weak signal as a rare, semantically coherent subgraph that proliferates across consecutive TKG snapshots. The framework operates through two modules: a Rare Subgraphs Extractor, in which an LLM identifies subgraphs whose content is in tension with the dominant snapshot narrative via chain-of-thought reasoning, and a Weak Signal Alerter, in which the persistence of these rare subgraphs is tracked across time steps to isolate true weak signals. Experimental results demonstrate that C-Unseen outperforms keyword-, topic-, and graph-based baselines.
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