While financially preferable, pre-silicon hardware Trojan (HT) detection remains a primary security challenge in modern integrated circuits (ICs). In this paper, a pre-silicon framework is developed for identifying rarely triggered nets (including those of HTs). Unsupervised knowledge graph embedding is utilized to transform conditional triggering probability of IC nets into the Euclidean distance between the nets’ embeddings. The proposed approach is not limited by HT types/IC sizes and reference-free. The framework is evaluated with TrustHub benchmarks, fully supporting the theoretical results: HTs are identified in the center of embeddings’ cloud, reducing the HT search space by over 10X.