Title: Fine-Grained Source Code Vulnerability Detection via Graph Neural Networks. Abstract: The number of exploitable vulnerabilities in software continues to increase, the speed of bug fixes and software updates have not increased accordingly. It is therefore crucial to analyze the source code and identify vulnerabilities in the early phase of software development. In this paper, a fine-grained source code vulnerability detection model based on Graph Neural Networks (GNNs) is proposed with the aim of locating vulnerabilities at the function level and line level. First of all, detailed information about the source code is extracted through multi-dimensional program feature encoding to facilitate learning about patterns of vulnerability. Second, extensive experiments are conducted on both a public hybrid dataset and our proposed dataset, which is collected entirely from real software projects. It is demonstrated that our proposed model outperforms the state-of-the-art methods and achieves significant improvements even when faced with more complex real-project source code. Finally, a novel location module is designed to identify potential key vulnerable lines of code. And the effectiveness of the model and its contributions to reducing human workload in practical production are evaluated.