گرافی که فکر می‌کند

Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017).
Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18–42.

Hamilton, W. L., Ying, R., & Leskovec, J. (2017).
Inductive representation learning on large graphs. Advances in Neural Information Processing Systems (NeurIPS).

Kipf, T. N., & Welling, M. (2017).
Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR).

Li, Q., Han, Z., & Wu, X. (2018).
Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the AAAI Conference on Artificial Intelligence.

Oono, K., & Suzuki, T. (2020).
Graph neural networks exponentially lose expressive power for node classification. International Conference on Learning Representations (ICLR).

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021).
A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.

Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019).
GNNExplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems (NeurIPS).

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2018).
Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.

Distill. (2021).
A gentle introduction to graph neural networks. Distill.
https://distill.pub/2021/gnn-intro/

https://www.sciencedirect.com/science/article/abs/pii/S1361841521002784

https://arxiv.org/abs/2411.05815?utm_source=chatgpt.com

https://arxiv.org/html/2411.09999v1?utm_source=chatgpt.com