Johnson, S., Torres, J. J., Marro, J., and Munoz, M. A. Entropic origin of disassortativity in complex networks. Physical Review Letters, 104(10):108702, 2010.
Jones, E., Oliphant, T., Peterson, P., et al. SciPy: Open source scientific tools for Python, 2001. URL http://www.scipy.org/.
Kipf, T. N. and Welling, M. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations, 2017.
Kuang, D., Ding, C., and Park, H. Symmetric nonnegative matrix factorization for graph clustering. In Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 106-117. SIAM, 2012.
Kuang, D., Yun, S., and Park, H. Symnmf: nonnegative low-rank approximation of a similarity matrix for graph clustering. Journal of Global Optimization, 62(3):545-574, 2015.
Lee, D. D. and Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
Liu, D. C. and Nocedal, J. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1-3):503-528, 1989.
Mason, O. and Verwoerd, M. Graph theory and networks in biology. IET Systems Biology, 1(2): 89-119, 2007.
McPherson, M., Smith-Lovin, L., and Cook, J. M. Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1):415-444, 2001.
Miller, K. T., Griffiths, T. L., and Jordan, M. I. Nonparametric latent feature models for link prediction. In Advances in Neural Information Processing Systems 22, pp. 1276-1284. Curran Associates, Inc., 2009.
Nascimento, M. C. and De Carvalho, A. C. Spectral methods for graph clustering-a survey. European Journal of Operational Research, 211(2):221-231, 2011.
Newman, M. E. Assortative mixing in networks. Physical Review Letters, 89(20):208701, 2002.
Noldus, R. and Van Mieghem, P. Assortativity in complex networks. Journal of Complex Networks, 3(4):507-542, 2015.
Nourbakhsh, F., Bulo, S. R., and Pelillo, M. A matrix factorization approach to graph compression. In 2014 22nd International Conference on Pattern Recognition, pp. 76-81. IEEE, 2014.
NT, H. and Maehara, T. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550, 2019.
Pashanasangi, N. and Seshadhri, C. Faster and generalized temporal triangle counting, via degeneracy ordering. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1319-1328, 2021.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pp. 8024-8035. Curran Associates, Inc., 2019.
Pei, H., Wei, B., Chang, K. C., Lei, Y., and Yang, B. Geom-gcn: Geometric graph convolutional networks. In 8th International Conference on Learning Representations, ICLR 2020, 2020.
Pérez, F. and Granger, B. E. IPython: a system for interactive scientific computing. Computing in Science and Engineering, 9(3):21-29, May 2007. ISSN 1521-9615. doi: 10.1109/MCSE.2007.53. URL https://ipython.org.
Peysakhovich, A. and Bottou, L. An attract-repel decomposition of undirected networks. arXiv preprint arXiv:2106.09671, 2021.