The widespread availability of Large Language Models (LLMs) such as GPT-4 and Llama-3, among others, has led to a surge in machine-generated content across various platforms, including social media, educational tools, and academic settings. While these models demonstrate remarkable capabilities in generating coherent text, their misuse raises significant concerns. For this reason, detecting machine-generated text has become a pressing need to mitigate these risks. This research proposed a novel classification method combining text-graph representations with Graph Neural Networks (GNNs) and different node feature initialization strategies to distinguish between human-written and machine-generated content. Experimental results demonstrate that the proposed approach outperforms traditional machine learning classifiers, highlighting the effectiveness of integrating structural and semantic relationships in text.