## 5 Conclusions and Future Work
### Future Work  
The ESA-GCN model has achieved better performance in handling imbalanced node classification. In future work, the following directions can be further explored:  
* More effective minority class sample selection strategies: Although ENN undersampling can reduce the interference of noisy samples, it may also result in the loss of useful minority class samples. Therefore, it is necessary to explore more refined strategies for selecting minority class samples to better utilize their information.
* More efficient feature learning methods: Current algorithms mainly utilize self-attention mechanisms for feature learning of minority class samples. In future work, other more efficient or precise feature learning methods can be explored to further improve the model's performance.
* More complex graph data classification tasks: Current experiments are primarily focused on node classification tasks. In future work, more complex graph data classification tasks such as graph matching and community detection can be explored, and the improved algorithms can be validated on these tasks.
* Extension to other application domains: This study primarily conducted experiments on citation networks, but there are many other real-world applications that can be regarded as having imbalanced node classification problems. Therefore, we hope to extend our framework to more application domains, such as cancer diagnosis, malicious code detection, oil spill detection, and improper social account detection, in order to address specific application scenarios.  
Conceptualization, methodology: L.Z. and H.S.; software, visualization, formal analysis, writing--original draft preparation: H.S.; writing--review and editing, supervision, funding acquisition: L.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China, Grant No. 72374210.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz)]([https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz)), [[https://linqs-data.soe.ucsc.edu/public/lbc/citeseer.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/citeseer.tgz)]([https://linqs-data.soe.ucsc.edu/public/lbc/citeseer.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/citeseer.tgz)), [[https://pubmed.ncbi.nlm.nih.gov/download/](https://pubmed.ncbi.nlm.nih.gov/download/)]([https://pubmed.ncbi.nlm.nih.gov/download/](https://pubmed.ncbi.nlm.nih.gov/download/)).  
We would like to express our sincere gratitude to all the individuals and institutions who have contributed to this paper. We especially appreciate the contributions made by Y.S. and B.S. in the areas of writing, review, and editing. Furthermore, we would like to thank the funding institutions, including Xinzhu Zheng, [PERSON] and [PERSON], for their financial support.  
We are deeply grateful to all the individuals and institutions mentioned above for their support and assistance, which have enabled us to complete this paper.  
The research was conducted without any commercial or financial conflict of interest.