Risk Assessment of Unexpected Events in Universities: Graph Theory Algorithms in Complex System Networks

With the continuous development of society, the risk assessment of unexpected events in universities has become increasingly important. This study aims to explore the application of graph theory algorithms in complex system networks, in order to improve the accuracy and reliability of emergency risk assessment in universities. This article considers various related factors, including various interaction relationships within and outside the school, introduces graph theory algorithms, and identifies potential risk factors and vulnerabilities by analyzing the network structure and the relationships between nodes. This article found through empirical case studies that the average prediction accuracy of graph theory algorithms in the application of complex system networks can reach 93.34%, demonstrating the effectiveness of graph theory algorithms and providing new ideas and methods for risk management of unexpected events in universities.