Distributed integrated circuit (IC) supply chain has resulted in a myriad of security vulnerabilities including that of hardware Trojan (HT). An HT can perform malicious modifications on an IC design with potentially disastrous consequences, such as leaking secret information in cryptographic applications or altering operation instructions in processors. Due to the emergence of outsourced fabrication, an untrusted foundry is considered the most potent adversary in introducing an HT. In order to address this issue, in this paper, we introduce a layout-level HT detection algorithm utilizing low-confidence classification and providing Trojan localization. We convert the IC layout to a graph and utilize Graph Neural Network (GNN)-based learning frameworks to flag any unrecognized suspicious region in the layout. The proposed framework is evaluated on AES and RS232 designs from the Trusthub benchmark suite, where it has been demonstrated to detect all nine HT-inserted designs. Finally, we open-source the full code-base for the research community at large.