D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, Convolutional networks on graphs for learning molecular fingerprints, in Proc. 28th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2015, pp. 2224-2232.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, Neural message passing for quantum chemistry, in Proc. 34th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 1263-1277.
M. D. Cranmer, R. Xu, P. Battaglia, and S. Ho, Learning symbolic physics with graph networks, arXiv preprint arXiv: 1909.05862, 2019.
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia, Graph networks as learnable physics engines for inference and control, in Proc. 35th Int. Conf. Machine Learning, Stockholm, Sweden, 2018, pp. 4470-4479.
T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, in Proc. 5th Int. Conf. Learning Representations, Toulon, France, 2017, .
X. N. He, K. Deng, X. Wang, Y. Li, Y. D. Zhang, and M. Wang, LightGCN: Simplifying and powering graph convolution network for recommendation, in Proc. 43rdInt. ACM SIGIR Conf. Research and Development in Information Retrieval, .
V. P. Dwivedi, C. K. Joshi, T. Laurent, Y. Bengio, and X. Breson, Benchmarking graph neural networks, arXiv preprint arXiv: 2003.00982, 2020.
Z. W. Zhang, P. Cui, and W. W. Zhu, Deep learning on graphs: A survey, IEEE Transactions on Knowledge and Data Engineering, .
P. Velickovie, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph attention networks, in Proc. 6th Int. Conf. Learning Representations, Vancouver, Canada, 2018, .
K. Xu, W. H. Hu, J. Leskovec, and S. Jegelka, How powerful are graph neural networks? in Proc. 7th Int. Conf. Learning Representations, New Orleans, LA, USA, 2019, .
H. M. Zhu, F. L. Feng, X. N. He, X. Wang, Y. Li, K. Zheng, and Y. D. Zhang, Bilinear graph neural network with neighbor interactions, in Proc. 29th Int. Joint Conf. Artificial Intelligence, Yokohama, Japan, 2020, pp. 1452-1458.
C. Shi, B. B. Hu, W. X. Zhao, and P. S. Yu, Heterogeneous information network embedding for recommendation, IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 357-370, 2019.
X. Wang, M. Q. Zhu, D. Y. Bo, P. Cui, C. Shi, and J. Pei, AM-GCN: Adaptive multi-channel graph convolutional networks, in Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Diego, CA, USA, 2020, pp. 1243-1253.
X. Wang, H. Y. Ji, C. Shi, B. Wang, Y. F. Ye, P. Cui, and P. S. Yu, Heterogeneous graph attention network, in Proc. of the World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2022-2032.
W. J. Chen, Y. L. Gu, Z. C. Ren, X. N. He, H. T. Xie, T. Guo, D. W. Yin, and Y. D. Zhang, Semi-supervised user profiling with heterogeneous graph attention networks, in Proc. 28th Int. Joint Conf. Artificial Intelligence, Macao, China, 2019, pp. 2116-2122.
H. T. Hong, H. T. Guo, Y. C. Lin, X. Q. Yang, Z. Li, and J. P. Ye, An attention-based graph neural network for heterogeneous structural learning, in Proc. 34th AAAI Conf. Artificial Intelligence, New York, NY, USA, 2020, pp. 4132-4139.
S. Zhang and L. Xie, Improving attention mechanism in graph neural networks via cardinality preservation, in Proc. 29th Int. Joint Conf. Artificial Intelligence, New York, NY, USA, 2020, pp. 1395-1402.
B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online learning of social representations, in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 701-710.
J. Tang, M. Qu, M. Z. Wang, M. Zhang, J. Yan, and Q. Z. Mei, LINE: Large-scale information network embedding, in Proc. 24th Int. Conf. World Wide Web, Florence, Italy, 2015, pp. 1067-1077.
Y. X. Dong, N. V. Chawla, and A. Swami, Metapath2vec: Scalable representation learning for heterogeneous networks, in Proc. 23rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 135-144.
Z. N. Hu, Y. X. Dong, K. S. Wang, and Y. Z. Sun, Heterogeneous graph transformer, in Proc. Web Conf., Taipei, China, 2020, pp. 2704-2710.
X. Y. Fu, J. N. Zhang, Z. Q. Meng, and I. King, MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding, in Proc. Web Conf., Taipei, China, 2020, pp. 2331-2341.