Distributionally-adaptive variational meta learning for brain graph classification

Recent developments in Graph Neural Networks (GNNs) have shed light on understanding brain networks through innovative approaches. Despite these innovations, the significant costs associated with data collection and the challenges posed by data drift in real-world scenarios present substantial challenges for models dependent on large datasets to capture brain activity features. To address these issues, we propose the Distributionally-Adaptive Variational Meta Learning (DAML) framework, designed to equip the model with rapid adaptability to varying distributions by meta learning-driven minimization of discrepancies between subject sets. Initially, we employ a graph encoder with the message-passing strategy to generate precise brain graph representations. Subsequently, we implement a distributionally-adaptive variational meta learning approach to functionally simulate data drift across subject sets, utilizing variational layers for parameterization and the adaptive alignment method to reduce discrepancies. Through comprehensive experiments on three real-world datasets with both small-data and standard regimes against various baselines, our DAML model demonstrates the state-of-the-art performance across all metrics, underscoring its efficiency and potential within limited data.