arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read topology and fail on cold nodes; LLMs read text and fail on text-ambiguous nodes. Existing LLM-GNN methods all follow the same recipe: designate one model as the golden teacher and use its outputs (e.g., features or pseudo-labels) to supervise the oth
The increasing complexity of real-world text-attributed graphs and advancements in both GNN and LLM technologies make co-teaching approaches a natural next step for better performance on few-shot tasks.
This development proposes a more sophisticated integration of LLMs and GNNs, moving beyond simple 'teacher-student' paradigms, which could significantly improve AI performance in understanding complex, interconnected data structures critical to many applications.
Traditional LLM-GNN integration will evolve from a 'golden teacher' model to more dynamic, co-teaching frameworks, leading to more robust and accurate learning on text-attributed graphs, particularly in data-scarce scenarios.
- · AI researchers and developers
- · Social media analytics platforms
- · E-commerce recommendation systems
- · Cybersecurity for graph intrusion detection
- · AI models relying solely on GNNs for semantic understanding
- · AI models relying solely on LLMs for topological understanding
- · Static 'teacher-student' model integration frameworks
Improved performance of AI systems on complex, text-attributed graph data with limited labels.
Accelerated development of more adaptive and context-aware AI agents capable of understanding and navigating intricate real-world networks.
Enhanced intelligence and autonomy of systems operating in domains like misinformation detection, fraud analysis, and personalized information retrieval.
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Read at arXiv cs.LG