Niepert, M., Ahmed, M., & Kutzkov, K. (2016, June). Learning convolutional neural networks for graphs. In International conference on machine learning (pp. 2014-2023).
 Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., … & Guo, M. (2018, April). Graphgan: Graph representation learning with generative adversarial nets. In Thirty-Second AAAI Conference on Artificial Intelligence.
 Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2019). A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596.
 Tang, L., & Liu, H. (2009, June). Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 817-826). ACM.
 Perozzi, B., Al-Rfou, R., & Skiena, S. (2014, August). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701-710). ACM.
 Wang, D., Cui, P., & Zhu, W. (2016, August). Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1225-1234). ACM.
 Wu, L., Sun, P., Hong, R., Fu, Y., Wang, X., & Wang, M. (2018). SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. arXiv preprint arXiv:1811.02815.
A strong hardware memory model is one in which every machine instruction comes implicitly with acquire and release semantics. As a result, when one CPU core performs a sequence of writes, every other CPU core sees those values change in the same order that they were written.