Graph Neural Networks (GNNs) excel at analyzing relationships within complex datasets, but they often falter when labeled data is scarce. This is a significant hurdle, as many real-world scenarios, from social networks to scientific literature, involve sparsely labeled graphs. Imagine trying to understand a social network where only a handful of users have publicly declared their interests. Traditional GNNs struggle to infer the interests of the remaining users due to this lack of information. Now, enter Large Language Models (LLMs). Researchers have discovered a way to use LLMs to empower GNNs in these data-starved situations. LLMs, trained on vast text corpora, bring a wealth of 'zero-shot' knowledge. This means they can make reasonable predictions about unseen data without specific training. In this new approach, the LLM acts as a teacher, guiding the GNN student. It does so in two ways: providing predicted labels for unlabeled nodes and offering explanations for its reasoning. Think of it like a tutor explaining not just the answer but the logic behind it. The GNN then uses this knowledge to enhance its understanding of the graph's structure and relationships. Furthermore, a clever active learning strategy is employed. This involves selecting the nodes where the GNN is most uncertain and asking the LLM for its expert opinion. This targeted approach maximizes the benefit gained from each LLM query. This synergistic combination of LLMs and GNNs has shown remarkable results, significantly outperforming traditional GNNs in few-shot learning scenarios. The results suggest a future where LLMs play a crucial role in unlocking the power of GNNs for a wider range of real-world applications, especially those dealing with limited labeled data.