Multimodal Graph Representation Learning

Real-world graph data is enriched with text, images, audio, and other modalities that provide information about nodes and relationships. This chapter explores Multimodal Graph representation Learning as a unified approach for integrating these diverse data sources into a coherent graph learning framework. It introduces different types of modalities commonly associated with graph data and explains how each contributes unique semantic and contextual signals. The chapter then presents data fusion techniques that combine multimodal features to produce more informative and robust graph representations. Practical applications in autonomous vehicles and agriculture, particularly pest detection networks, illustrate how multimodal graph learning enhances perception, decision-making, and prediction accuracy. Finally, it highlights how multimodal GRL enables richer modeling of complex, real-world systems.