Open Access
Issue
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
Article Number 00096
Number of page(s) 10
DOI https://doi.org/10.1051/e3sconf/202560100096
Published online 16 January 2025
  1. W. Hamilton, “Inductive Representation Learning on Large Graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS), 2017. [Google Scholar]
  2. Xiao Li, Li Sun, Mengjie Ling, Yan Peng, “A survey of graph neural network based recommendation in social networks”, Neurocomputing, Volume 549, 2023. [Google Scholar]
  3. X. He, “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. [Google Scholar]
  4. J. Wang, H. Xie, F. L. Wang, L.-K. Lee, and O. T. S. Au, “Top-N Personalized Recommendation with Graph Neural Networks in MOOCs,” *Computers and Education: Artificial Intelligence*, vol. 2, 2021. [Google Scholar]
  5. Zhang X.-M., Liang L., Liu L. and Tang M.-J. (2021) “Graph Neural Networks and Their Current Applications in Bioinformatics”. Front. Genet. 12:690049 [CrossRef] [Google Scholar]
  6. T. N. Kipf, M. Welling “Semi-Supervised Classification with Graph Convolutional Networks,” 2017. [Google Scholar]
  7. M. Zhang, “Link Prediction Based on Graph Neural Networks,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS), 2018. [Google Scholar]
  8. A. Tsitsulin, J. Palowitch, B. Perozzi, and E. Müller, “Graph Clustering with Graph Neural Networks,” arXiv:2006.16904, 2023. [Online]. Available: https://arxiv.org/abs/2006.16904 [Google Scholar]
  9. P. Velickovic et al., “Graph Attention Networks,” in International Conference on Learning Representations (ICLR), 2018. [Google Scholar]
  10. Huang, Q., Zeng, Y. “Improving academic performance predictions with dual graph neural networks”. Complex Intell. Syst. 10, 3557–3575 (2024). [CrossRef] [Google Scholar]
  11. Y. Zuo, H. Luo, and L. Xu, “Enhancing MOOCs Personalized Recommendation with Graph Neural Networks and Attention Mechanisms,”, 2023. [Google Scholar]
  12. J. Chen, “Graph Neural Networks for Enhanced E-Learning Systems,” Journal of Educational Technology Systems, vol. 50, no. 1, pp. 20–35, 2021. [Google Scholar]
  13. R. Ying, “Graph Convolutional Neural Networks for Web-Scale Recommender Systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2018. [Google Scholar]
  14. A. Defilippo et al., “Leveraging graph neural networks for supporting Automatic Triage of Patients,” Nature, 2024. [Google Scholar]
  15. J. Klicpera, A. Bojchevski and S. Günnemann, “Predict then Propagate: Graph Neural Networks meet Personalized PageRank,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. [Google Scholar]
  16. X. Wang, “Heterogeneous Graph Attention Network,” in Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM), 2019. [Google Scholar]
  17. L. Zhao, “PairNorm: Tackling Oversmoothing in GNNs,” in Proceedings of the 8th International Conference on Learning Representations (ICLR), 2019. [Google Scholar]
  18. M. Bronstein, “Geometric Deep Learning: Going beyond Euclidean data,” IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18–42, 2017. [CrossRef] [Google Scholar]
  19. M. Defferrard, “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS), 2016. [Google Scholar]
  20. K. Wang, “EdNet-KT1,” Kaggle, 2020. [Online]. Available: https://www.kaggle.com/datasets/gmhost/ednetkt1. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.