# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 3 Methods
### EGNN + Node Hierarchical Residual Model Architecture
Therefore, by cleverly designing hierarchical structures, the model can better adapt to different few-shot learning tasks. ## 5 Conclusions

This study involved the introduction of residual connection structures into the Edge-Conditioned Graph Neural Network (EGNN). This innovative strategy, inspired by the principles of deep residual networks (ResNet), aimed to enhance the model's information propagation and learning capabilities. Initially designed for few-shot image classification tasks, EGNN had demonstrated remarkable performance in handling limited data. Figure 6: Image classification accuracy of EGNN-NHR0 on mini-ImageNet-5-way-1-shot. From left to right: L5, L4, L3, with the highest accuracies being 62.23%, 62.02%, 61.61% respectively. However, in the mimicked work, the introduction of residual connections between different graph neural layers within EGNN significantly increased the model's depth and complexity. This adaptation allowed the model to better capture the features and relationships within the data. This mimicking approach bears resemblance to the practice of incorporating residual blocks in ResNet, a widely acknowledged innovation in the field of deep learning. Consequently, the mimicked work involved the integration of residual connections into EGNN, leading to improved information propagation and learning capabilities. This strategy yielded substantial improvements in accuracy, particularly in the two primary benchmark metrics of 5-way 1-shot and 5-way 5-shot tasks in both public datasets and custom datasets. As for the scope for improvement, future research can explore further extensions of this concept. Experimenting with different types of residual structures or combinations could potentially enhance the model's performance. Additionally, investigating the applicability of this residual connection strategy in other tasks and domains would provide insights into its versatility and utility. Further improvements in model training and optimization techniques may also be explored to enhance performance while reducing computational complexity. These directions represent potential avenues for future research, offering the opportunity to advance the fields of graph neural networks and few-shot learning. ## 6 Innovation and Outlook

In few-shot classification, uncertainty is a critical issue as models need to better estimate the confidence of their predictions. Future research may focus on how to effectively model prediction uncertainty in Graph Neural Networks, thereby providing more reliable decision support. The model architecture of graph neural networks is still evolving, and new structures more suitable for few-shot classification may emerge in the future. These structures may better capture relationships and local patterns in graph data, thus enhancing model performance. The integration of hierarchical residual techniques in this study injects new vitality and innovation into the field of graph neural networks for few-shot classification. By propagating residual information at each layer, the model can flexibly learn hierarchical features within the data, thereby improving its generalization and classification performance. This approach holds the promise of addressing the challenge of gradient propagation in graph data and facilitating the orderly transmission of information within complex graph structures. Hierarchical residuals also endow Graph Neural Networks with stronger transfer learning and domain adaptation capabilities, opening up greater potential for various practical application scenarios. In the future, we believe that the innovation of different hierarchical residuals at different layers will lead the development of graph neural networks in the field of few-shot classification, providing more accurate classification results in data-scarce scenarios. Conceptualization, Y.W. and Y.X. ; methodology, Y.W. ; software and validation, Y.X. and Y.W. ; writing--review and editing, Y.W. ; supervision, Y.X. and Y.W. ; project administration, Y.X. ; and funding acquisition, Y.W.