Towards Multi-modal Graph Large Language Models Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are typically trained from scratch for specific graph data and tasks, failing to generalize across various multi-modal graph data and tasks. To bridge this gap, new research explores the potential of Multi-modal Graph Large Language Models (MG-LLM) to unify and generalize across diverse multi-modal graph data and tasks. It proposes a unified framework of multi-modal graph data, task, and model, discovering the inherent multi-granularity and multi-scale characteristics in multi-modal graphs. Specifically, it presents five key desired characteristics for MG-LLM: 1) unified space for multi-modal structures and attributes, 2) capability of handling diverse multi-modal graph tasks, 3) multi-modal graph in-context learning, 4) multi-modal graph interaction with natural language, and 5) multi-modal graph reasoning. The paper elaborates on the key challenges, review related works, and highlight promising future research directions towards realizing these ambitious characteristics. Finally, it summarizes existing multi-modal graph datasets pertinent for model training. The key insight is treating all graph tasks as generative problems. Instead of training separate models for node classification, link prediction, or graph reasoning, MG-LLM frames everything as transforming one multi-modal graph into another. A contribution to the ongoing advancement of the research towards MG-LLM for generalization across multi-modal graph data and tasks. arxiv.org/abs/2506.09738v1 #AI #GenAI #EmergingTech #MultimodalGraphs #GraphLLM #NeuralNetworks #MachineLearning #GraphML #DeepLearning #DataScience #Research #GraphTransformers #LLMs -- Connected Data London 2025 has been announced! 20-21 November, Leonardo Royal Hotel London Tower Bridge Join us for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology 🎟️ Ticket sales are open. Benefit from early bird prices with discounts up to 30%. 2025.connected-data.london 📋 Call for submissions is open. Check topics of interest, submission process and evaluation criteria connected-data.london/call-f… 📺 Sponsorship opportunities are available. Maximize your exposure with early onboarding. Contact us at info@connected-data.london for more.