Machine learning on multimodal graphs, referred to as multimodal graph learning (MGL), is essential for successful artificial intelligence (AI) applications. The burgeoning research in this field encompasses diverse graph data types and modalities, learning techniques, and application scenarios. This ...
Learning on multimodal datasets is challenging because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, graph artificial intelligence methods combine different modalities while leveraging cross-modal dependencies through ...