In many domains, relationships between categories are encoded in the knowledge graph. Recently, promising results have been achieved by incorporating knowledge graphs as a side-information in hard classification tasks with severely limited data. However, prior models consist of highly complex architectures with many sub-components that all seem to impact performance. In this paper, we present a comprehensive empirical study on graph embedded few-shot learning. We introduce a graph regularization approach that allows deeper understanding of the impact of incorporating graph information between labels. Our proposed regularization is widely applicable and model-agnostic, and boosts performance of any few-shot learning model, including metric-learning, meta-learning, and fine-tuning. Our approach improves strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS, outperforming state-of-the-art models and other graph embedded methods. Additional analyses reveal that graph regularizing models results in lower loss for more difficult tasks such as lower-shot and less informative few-shot episodes.