A Graph Representation Learning-Based Method for Event Prediction

Abstract

With the continuous advancement of big data and artificial intelligence technologies, event prediction is increasingly being utilized across a multitude of domains. Predicting events allows for the exploration of the developmental trajectories and summarization of patterns associated with these events. However, events typically encompass a myriad of elements and intricate relationships, necessitating an enhancement in the precision of event prediction. However, the existing methods suffer from poor data quality, insufficient feature information, limited generalization capability of the models, and difficulties in evaluating prediction errors. This paper proposes a novel event prediction method based on graph representation learning, aiming to improve the accuracy of event prediction while reducing the time cost. By constructing causal graphs and introducing the script event simulation method, the architecture combines graph neural networks (GNNs) with BERT to simplify the event prediction process. Additionally, by combining GNNs with pretrained language models, a dynamic graph representation learning method is proposed. This means that a unified graph representation learning model can be built by following specific rules, thus predicting the development trajectory of events more accurately. The study evaluates the effectiveness of dynamic graph representation learning technology in a specific scenario, specifically in the context of employee career choices. By converting the career graph of employees into low-dimensional representations, the effectiveness of the dynamic graph representation learning method in predicting employee career decisions is validated. This innovation not only improves the accuracy of event prediction but also helps better understand and respond to complex event relationships in practical applications, providing decision-makers with more powerful information support. Therefore, this research has important theoretical and practical significance, providing valuable references for future studies in related fields.