MOVE: Multimodal Open-world Verification and Expansion for Graph Learning
Read the original on arXiv Machine Learning →The paper introduces MOVE, a framework for multimodal open‑world verification and expansion in graph learning. MOVE jointly uses visual tokens, textual attributes, and graph context to identify nodes that cannot be assigned to existing classes, then employs a multimodal LLM to generate candidate class descriptions. It selectively expands the class space only when multimodal evidence consistently supports the new classes, avoiding redundancy, and reports an average 11.87% improvement across unknown recognition, open‑domain annotation, and downstream graph learning tasks.
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