A Feature-Aware Approach to Acupoint Compatibility Prediction Using Residual Graph Attention Networks and Matrix Factorization

Compatibility among acupoints is a fundamental principle in acupuncture treatment within traditional Chinese medicine, playing a vital role in enhancing the effectiveness and scope of therapeutic interventions. With the increasing availability of acupuncture-related data, link prediction offers a data-driven approach that facilitates the evidence-based exploration and validation of acupoint compatibilities. However, existing link prediction methods often focus on mapping acupoints and their compatibility relationships into lower-dimensional spaces. These approaches can overlook essential acupoint features and make the predictions susceptible to noise interference. To address these challenges, we propose a novel acupoint compatibility prediction model based on a Feature-Aware Residual Graph Attention Network and Matrix Factorization (FRGATMF). Our model introduces a feature-aware connectivity fusion strategy that integrates acupoint attributes with structural information to enrich acupoint representations. Following this, a deep non-negative matrix factorization approach is employed to construct a denoised feature matrix. This matrix is processed through a residual graph attention network to derive comprehensive and effective node embeddings, which are crucial for accurate link prediction. Experimental results on the acupuncture dataset, along with three public datasets, demonstrate that FRGATMF significantly outperforms seven existing comparison models across various evaluation metrics. Additionally, link prediction can identify previously unconsidered or undocumented acupoint combinations that may offer better therapeutic results, thus expanding the range of treatment options and highlighting its potential in improving the prediction of acupoint compatibility relationships.