multimodal-graph-learning

MOVE is a multimodal open-world class verification and expansion framework for graph learning that jointly uses visual tokens, textual attributes, and graph context to detect unknown nodes, generates candidate classes via a multimodal LLM, and selectively expands the class space, achieving an average 11.87% improvement across unknown recognition, open-domain annotation, and downstream tasks.

This paper proposes FedMPO, a robust federated multimodal graph learning method that addresses modality heterogeneity and missing modalities through topology-aware cross-modal generation, missing-aware expert routing, and reliability-aware aggregation, achieving performance gains on multiple datasets.