摘要：
[TOC] > [Xia X., Yin H., Yu J., Wang Q., Cui L and Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. AAAI, 阅读全文
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[TOC] > [Xia X., Yin H., Yu J., Shao Y. and Cui L. Self-supervised graph co-training for session-based recommendation. CIKM, 2021.](http://arxiv.org/a 阅读全文
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[TOC] > [Wang Z., Wei W., Cong G., Li X., Mao X. and Qiu M. Global context enhanced graph neural networks for session-based recommendation. SIGIR, 202 阅读全文
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[TOC] >[ Li J., Ren P., Chen Z., Ren Z., Lian T. and Ma J. Neural attentive session-based recommendation. CIKM, 2017.](http://arxiv.org/abs/1711.04725 阅读全文
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[TOC] > [Liu Q., Zeng Y., Mokhosi R. and Zhang H. STAMP: Short-term attention/memory priority model for session-based recommendation. KDD, 2018.](http 阅读全文
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[TOC] > [Niu C., Song Y., Song J., Zhao S., Grover A. and Ermon S. Permutation invariant graph generation via score-based generative modeling. AISTATS 阅读全文
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[TOC] > [Liao R., Li Y., Song Y., Wang S., Nash C., Hamilton W. L., Duvenaud D., Urtasun R. and Zemel R. NIPS, 2019.](http://arxiv.org/abs/1910.00760) 阅读全文
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[TOC] > [Liu J., Kumar A., Ba J., Kiros J. and Swersky K. Graph normalizing flows. NIPS, 2019.](http://arxiv.org/abs/1905.13177) ## 概 基于 [flows](https 阅读全文
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[TOC] > [Dinh L, Sohl-Dickstein J. and Bengio S. Density estimation using real nvp. ICLR, 2017.](http://arxiv.org/abs/1605.08803) ## 概 一种可逆的 flow, 感觉很 阅读全文
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[TOC] > [Zhou D., Bousquet O., Lal T. N., Weston J. and Scholk\ddot{o}pf B. Learning with local and global consistency. NIPS, 2004.](https://proceedin 阅读全文
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[TOC] > [Huang Q., He H., Singh A., Lim S. and Benson A. R. Combining label propagation and simple models out-performs graph neural networks. ICLR, 20 阅读全文
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Ying C., Cai T., Luo S., Zheng S., Ke D., Shen Y. and Liu T. Do transformers really perform badly for graph representation? NIPS, 2021. 概 本文提出了一种基于图的 阅读全文
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Chen L., Li J., Peng Q., Liu Y., Zheng Z. and Yang C. Understanding structural vulnerability in graph convolutional networks. IJCAI, 2021. 概 mean 是在 G 阅读全文
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Zhao L. and Akoglu L. Pairnorm: tackling oversmoothing in gnns. ICLR, 2020. 概 本文提出了一种 normaliztion 方法用于解决 over-smoothing 问题. 符号说明 $\mathcal{G} = (\mat 阅读全文
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Yuan H. and Ji S. Structpool: structured graph pooling via conditional random fields. ICLR, 2020. 概 一种图的 pooling 方法, 我并没有搞懂其中的原理, 这里只是记录一下. 符号说明 $G$, 阅读全文
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Li G., Xiong C., Thabet A. and Ghanem B. DeeperGcn: all you need to train deeper gcns. arXiv preprint arXiv:2006.07739 概 本文介绍了一种连续可微的 aggregation func 阅读全文
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Liu M., Gao H. and Ji S. Towards deeper graph neural networks. KDD, 2020. 概 本文介绍了一种加深模型的方法. 符号说明 $G = (V, E)$, 图; $|V| = n$; $|E| = m$; $\bm{A} \in \m 阅读全文
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Graikos A., Malkin N., Jojic N. and Samaras D. Diffusion models as plug-and-play priors. NIPS, 2022. 概 有了先验分布 $p(\mathbf{x})$ (用一般的扩散模型去拟合), 我们总是像添加一些 阅读全文
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Tang J. and Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018. 概 序列推荐的经典之作, 将卷积用在序列推荐之上. 符号说明 $\ma 阅读全文
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Ethayarajh K., Choi Y. and Swayamdipta S. Understanding dataset difficulty with $\mathcal{V}$-usable information. ICML, 2022. 概 将 $\mathcal{V}$-inform 阅读全文