A unified comparison across six multimodal graph datasets shows that fine-tuned multimodal LLMs used as direct predictors achieve the highest node classification accuracy, even without graph structure input.
Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language models
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Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
A unified comparison across six multimodal graph datasets shows that fine-tuned multimodal LLMs used as direct predictors achieve the highest node classification accuracy, even without graph structure input.