By organizing multimodal graph information into hierarchical contexts before feeding it to an LLM, CHARM achieves zero-shot transfer across different graph domains—meaning it can work on new graphs without any labeled training data from those domains.
CHARM is a foundation model for graphs that combines text, images, and other data types without needing labeled examples in new domains. It uses hierarchical context around each node to capture relationships across different data types, then converts these into tokens that a large language model can understand.