Class-Driven Graph Attention Network for Multi-Label Time Series Classification in Mobile Health Digital Twins

Digital Twins for Mobile Networks (DTMN) can enhance mobile health (mHealth) by increasing diagnostic and monitoring capabilities. Classifying multi-label time series mHealth data in DTMN is challenging due to complex class relevance and feature extraction difficulties. This paper proposes a Class-Driven Graph Attention network learning framework (C-DGAM) for Multi-label classification of mHealth data in DTMN. C-DGAM captures the complex class relationships by constructing a unique class relevance graph for each time series. It uses a temporal context attention module to generates class representation vectors by fusing multi-dimensional features of time and class. Then, it dynamically models different relevance among the class representation vectors through a dynamic graph attention module which improves the performance of multi-label time series classification while maintaining a smaller parameter size and lower computational complexity. The mean Average Precision achieved by C-DGAM on two different multi-label time series datasets are 0.955 and 0.776, respectively, with corresponding F1 scores of 0.867 and 0.80. It demonstrates leading performance compared to existing state-of-art works. It provides more accurate and generalized algorithmic support for DMTN systems.