references
Chapter 1
1 Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P. (2017). Geometric deep learning: Going Beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18–42.
2 Wu, Z., Shirui, P., Chen, F., et al. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32, 4–24.
3 Deo, N. (1974). Graph Theory with Applications to Engineering and Computer Science, Prentice-Hall of India.
4 Luce, D., and Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 14, 95–116.
5 Jia, J., Baykal, C., Potluru, V. K., and Benson, A. R. (2021). Graph belief propagation networks. arXiv preprint arXiv:2106.03033.
6 Gärtner, T., Le, Q. V., and Smola, A. (2005). A short tour of kernel methods for graphs. https://api.semanticscholar.org/CorpusID:4854202
7 Zhukov, L. (2015, May 19). Network analysis: Lecture 17 (part 1). Label propagation on graphs [video]. https://youtu.be/hmashUPJwSQ
8 Keen, B. A. (2017, May 9). Isomap for dimensionality reduction in Python [blogpost]. https://mng.bz/ey8P.
9 Ying, R., He, R., Chen, K., et al. (2018). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.