Research on Risk Assessment Model for Social High-Risk Individuals Based on Graph Attention Network

Abstract

To better carry out early warning and control work for high-risk individuals in society, this paper proposes a risk assessment model based on graph attention networks. The model analyzes relevant background and relationship information of these individuals and constructs a knowledge graph accordingly. An improved graph attention mechanism is introduced to establish the risk assessment model. Real police character data was used to train and test the model, and experimental results indicated a prediction accuracy of 89.4%, with both accuracy and recall rates around 90%. This model can provide decision-making basis and technical support for early warning of public security personnel by identifying potential risks of high-risk individuals.