C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning

2026-08-27

AI Summary

本文提出C-Unseen框架，通过LLM推理在动态时间知识图谱中检测弱信号，解决了现有方法无法捕捉信号语义和关系结构的问题。

Abstract

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.

Problem

• weak signals
• Dynamic Temporal Knowledge Graphs
• semantic and relational structure

Innovation

• LLM Reasoning
• Dynamic Temporal Knowledge Graphs
• Weak Signal Detection
• Rare Subgraphs Extractor
• Weak Signal Alerter