Learning risk matrix from textual data through graph neural networks

Risk analysis is a key process in project management, aiming to evaluate and mitigate risks by quantifying the probability and severity of potential events. A common tool for this purpose is the risk matrix, which offers a structured visualization of risks. Despite its effectiveness, constructing risk matrices traditionally depends on domain experts to define event probabilities and severities, leading to scalability and consistency challenges. This paper introduces RMGNN (Risk Matrix Generation with Graph Neural Networks), a semi-supervised and transductive method for automating risk matrix construction. RMGNN models historical events as graph structures, leveraging graph neural networks and language models to estimate event probabilities and severities. The method integrates labeled and unlabeled data, reducing reliance on manual annotation while maintaining accuracy. The main contributions of this paper include: (1) a graph-based framework for semi-supervised and transductive risk matrix learning, (2) a unified approach for estimating event probability and severity from graph representations, and (3) the use of graph topology to enhance interpretability in risk assessment. Experiments on real-world datasets demonstrate the effectiveness of RMGNN in addressing challenges associated with traditional risk matrix construction, supporting improved risk analysis and decision-making processes.