Multi Modal Graph Neural Network

Multi-modal graph neural networks (GNNs) integrate data from diverse sources—like images, text, and sensor readings—to improve prediction and classification tasks. Current research focuses on developing sophisticated GNN architectures, often incorporating graph transformers or convolutional networks, to effectively model complex relationships within and between different data modalities, addressing challenges like data heterogeneity and scalability. These methods are proving valuable across various fields, including medical diagnosis (e.g., Alzheimer's, cancer subtype classification), e-commerce (livestream product retrieval), and brain disorder prediction, by enabling more accurate and insightful analyses than single-modality approaches.