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

arXiv:2608.26870 · cs.AI, cs.CL, cs.SI · Submitted 2026-08-27 · Read on arXiv

cs.AI, cs.CL, cs.SI

Submitted: 2026-08-27

Updated: 2026-08-27

Comments: Accepted at the AI4SE 2026 Special Track, held within the WISE 2026 Conference

Code: https://github.com/AuvaLab/itext2kg

License: http://creativecommons.org/licenses/by/4.0/

The gist: Weak signals are early, low-visibility indicators that precede significant changes before those changes become established.

Terminology

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.

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