摘要：
Bengio Y., L\acute{e}onard N. and Courville A. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv prepr 阅读全文
摘要：
Wu L., Yang Y., Zhang K., Hong R., Fu Y. and Wang M. Joint item recommendation and attribute inference: an adaptive graph convolutional network approa 阅读全文
摘要：
Liu Z., Meng L., Jiang F., Zhang J. and Yu P. S. Deoscillated graph collaborative filtering. arXiv preprint arXiv:2011.02100, 2020. 概 作者认为鉴于推荐数据集二部图的特 阅读全文
摘要：
Ying R., He R., Chen K., Eksombatchai P., Hamilton W. L. and Leskovec J. Graph convolutional neural networks for web-scale recommender systems. In ACM 阅读全文
摘要：
Ren Y., Tang H. and Zhu S. Unbiased learning to rank with biased continuous feedback. In International Conference on Information and Knowledge Managem 阅读全文
摘要：
Huang T., Dong Y., Ding M., Yang Z., Feng W., Wang X. and Tang J. MixGCF: an improved training method for graph neural network-based recommender syste 阅读全文
摘要：
Liu S., Ying R., Dong H., Lin L., Chen J., Wu D. How powerful is implicit denoising in graph neural networks? arXiv preprint arXiv: 2209.14514, 2022. 阅读全文
摘要：
Zhu M., Wang X., Shi C., Ji H. and Cui P. Interpreting and unifying graph neural networks with an optimization framework. In International World Wide 阅读全文
摘要：
Zhao L. and Akoglu L. Connecting graph convolution and graph pca. 2022. 概 从 graph-regularized PCA 角度提出一种 GCN 的 message passing layer. 符号说明 $\tilde{A}$ 阅读全文
摘要：
Liu X., Jin W., Ma Y., Li Y., Li Y., Liu H., Wang Y., Yan M. and Tang J. Elastic graph neural networks. In International Conference on Machine Learnin 阅读全文
摘要：
Mao K., Zhu J., Xiao X., Lu B., Wang Z. and He X. UltraGCN: ultra simplification of graph convolutional networks for recommendation. In International 阅读全文
摘要：
Peng S., Sugiyama K. and Mine T. SVD-GCN: A simplified graph convolution paradigm for recommendation. In International Conference on Information and K 阅读全文
摘要：
Tan J., Geng S., Fu Z., Ge Y., Xu S., Li Y. and Zhang Y. Learning and evaluating graph neural network explanations based on counterfactual and factual 阅读全文
摘要：
Zintgraf L. M., Cohen T. S., Adel T. and Welling M. Visualizing deep neural network decisions: prediction difference analysis. In International Confer 阅读全文
摘要：
Ying R., Bourgeois D., You J., Zitnik M. and Leskovec J. GNNExplainer: generating explanations for graph neural networks. In Advances in Neural Inform 阅读全文
摘要：
Dong Y., Liu N., Jalaian B. and Li J. EDITS: modeling and mitigating data bias for graph neural networks. In International World Wide Web Conference ( 阅读全文
摘要：
Lahoti P., Beutel A., Chen J., Lee K., Prost F., Thain N., Wang X. and CHi E. H. Fairness without demographics through adversarially reweighted learni 阅读全文
摘要：
Dai E. and Wang S. Towards self-explainable graph neural network. In International Conference on Information and Knowledge Management (CIKM), 2021. 概 阅读全文
摘要：
Zhu D., Zhang Z., Cui P. and Zhu W. Robust graph convolutional networks against adversarial attacks. In ACM International Conference on Knowledge Disc 阅读全文
摘要：
Xu K., Chen H., Liu S., Chen P., Weng T., Hong M. and Lin X. Topology attack and defense for graph neural networks: an optimization perspective. In In 阅读全文