Liu Z, Fang Y, Liu C, et al. Relative and absolute location embedding for few-shot node classification on graph. Proceedings of the AAAI conference on artificial intelligence. 2021;35(5):4267–4275.

Prakash SKA, Tucker CS. Node classification using kernel propagation in graph neural networks. Expert Syst Appl. 2021;174: 114655.

Article  Google Scholar 

Smirnov V, Warnow T. MAGUS: multiple sequence alignment using graph clustering. Bioinformatics. 2021;37(12):1666–72.

Article  Google Scholar 

Patel R, Guo Y, Alhudhaif A, et al. Graph-based link prediction between human phenotypes and genes. Math Probl Eng. 2021;2022.

Fan W, Ma Y, Li Q, et al. Graph neural networks for social recommendation. The world wide web conference. 2019;417–426.

Chami I, Ying Z, Ré C, et al. Hyperbolic graph convolutional neural networks. Adv Neural Inf Process Syst. 2019;32.

Li Q, Han Z, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the AAAI conference on artificial intelligence. 2018;32(1).

Wu F, Souza A, Zhang T, et al. Simplifying graph convolutional networks. International conference on machine learning. PMLR. 2019;6861–6871.

Wang X, Zhu M, Bo D, et al. Am-gcn: adaptive multi-channel graph convolutional networks. Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery & data mining. 2020;1243–1253.

Veličković P, Cucurull G, Casanova A, et al. Graph attention networks. arXiv preprint arXiv: 1710.10903. 2017.

Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203. 2013.

Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst. 2016;29.

Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. 2016.

Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs. Adv Neural Inf Process Syst. 2017;30.

Wu J, He J, Xu J. Net: degree-specific graph neural networks for node and graph classification. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019;406–415.

Abu-El-Haija S, Perozzi B, Kapoor A, et al. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. International conference on machine learning. PMLR. 2019;21–29.

Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014;701–710.

Grover A, Leskovec J. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016;855–864.

Perozzi B, Kulkarni V, Skiena S. Walklets: Multiscale graph embeddings for interpretable network classification. arXiv preprint arXiv:1605.02115. 2016;043238–23.

Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding. Proceedings of the 24th international conference on world wide web. 2015;1067–1077.

Song L, Smola A, Gretton A, et al. Supervised feature selection via dependence estimation. Proceedings of the 24th international conference on Machine learning. 2007;823–830.

Wang X, Ji H, Shi C, et al. Heterogeneous graph attention network. The world wide web conference. 2019;2022–2032.

Wang W, Liu X, Jiao P, et al. A unified weakly supervised framework for community detection and semantic matching. Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III 22. Springer International Publishing, 2018;218-230.

Meng Z, Liang S, Bao H, et al. Co-embedding attributed networks. Proceedings of the twelfth ACM international conference on web search and data mining. 2019;393–401.

Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11).