  This paper introduces a novel Functional Graph Convolutional Network (funGCN)
framework that combines Functional Data Analysis and Graph Convolutional
Networks to address the complexities of multi-task and multi-modal learning in
digital health and longitudinal studies. With the growing importance of health
solutions to improve health care and social support, ensure healthy lives, and
promote well-being at all ages, funGCN offers a unified approach to handle
multivariate longitudinal data for multiple entities and ensures
interpretability even with small sample sizes. Key innovations include
task-specific embedding components that manage different data types, the
ability to perform classification, regression, and forecasting, and the
creation of a knowledge graph for insightful data interpretation. The efficacy
of funGCN is validated through simulation experiments and a real-data
application.
