The continuous advancements in technology and consequently, the increasing demand for integrated circuits, have led designers to outsource parts of the production process and use services provided by third-party vendors, which in turn exposes the production process to security concerns such as hardware Trojans. In this paper, we propose a machine-learning model utilizing Graph Convolutional Networks to detect hardware Trojans in gate-level netlists. In this regard, the proposed model, FAST-GO, is trained based on the extracted graph from the netlist and presents an efficient and compact set of structural gate-level features to classify the graph nodes into Trojan or normal nodes. Utilizing a small number of features in the presence of an efficient dataset enables the model to boost scalability and makes it a sensitive model applicable for large netlists. Experimental evaluation shows that our model has been successful in detecting 95.38% of Trojan nodes in 15 various-size circuits from the Trust-Hub benchmark only in 0.84 seconds on average using an Intel Core i5-3230M 2.6 GHz Processor with 6 GB RAM. This shows that FAST-Go is an accurate detection method with remarkably improved performance considering all known HT detection mechanisms.