馒头and花卷

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： Gupta U., Ferber A. M., Dilkina B. and Steeg G. V. Controllable guarantees for fair outcomes via contrastive information estimation. AAAI, 2021. 概 本文提 阅读全文

摘要： Ma Y., Wang S., Aggarwal C. C. and Tang J. Graph convolutional networks with eigenpooling. KDD, 2019. 概 本文提出了一种新的框架, 在前向的过程中, 可以逐步将相似的 nodes 和他们的特征聚合在 阅读全文

摘要： Chen T. and Wong R. C. Handling information loss of graph neural networks for session-based recommendation. KDD, 2020. 概 作者发现图用在 Session 推荐中存在: lossy 阅读全文

摘要： Chiang W., Liu X., Si S., Li Y., Bengio S. and Hsieh C. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. 阅读全文

摘要： Cen Y., Zou X., Zhang J., Yang H., Zhou J. and Tang J. Representation learning for attributed multiplex heterogeneous network. KDD, 2019. 概 本文在 Attrib 阅读全文

摘要： Maron H., Ben-Hamu H., Shamir N. and Lipman Y. Invariant and equivariant graph networks. ICLR, 2019. 概 有些时候, 我们希望网络具有: 不变性 (Invariant): $$ f(PX) = f(X 阅读全文

摘要： 目录概符号说明MotivationLADIES代码 Zou D., Hu Z., Wang Y., Jiang S., Sun Y. and Gu Q. Layer-dependent importance sampling for training deep and large graph con 阅读全文

摘要： Wang X., Ji H., Shi C., Wang B., Cui P., Yu P. and Ye Y. Heterogeneous graph attention network. WWW, 2019. 概 Attention + 异构图. 符号说明 $\mathcal{G} = (\ma 阅读全文

摘要： Ren Y., Liu B., Huang C., Dai P., Bo L. and Zhang J. Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538, 2019. 概 本文介绍了异构图的一种无监督学习方法. 这里 阅读全文

摘要： 目录概符号说明MotivationFastGCN方差分析代码 Chen J., Ma T. and Xiao C. FastGCN: fast learning with graph convolutional networks via importance sampling. ICLR, 2018 阅读全文

摘要： Li Q., Han Z. and Wu X. Deeper insights into graph convolutional networks for semi-supervised learning. AAAI, 2018. 概 本文分析了 GCN 的实际上就是一种 Smoothing, 但是 阅读全文

摘要： 目录概符号说明Motivation本文方法代码 Chen J., Zhu J. and Song L. Stochastic training of graph convolutional networks with variance reduction. ICML, 2018. 概 我们都知道, 阅读全文

摘要： Li Z., Sun A. and Li C. DiffuRec: A diffusion model for sequential recommendation. arXiv preprint arXiv:2304.00686, 2023. 概 扩散模型用于序列推荐, 性能提升很大. DiffuR 阅读全文

摘要： Fan Z., Liu Z., Wang A., Nazari Z., Zheng L., Peng H. and Yu P. S. Sequential recommendation via stochastic self-attention. International World Wide W 阅读全文

摘要： Spielman D. A. Spectral and Algebraic Graph Theory. 概 设计 Hypercube 的特征值和特征向量的证明着实有趣, 特此记录. Hypercube 对于两个加权图 $G = (V, E, v)$ 和 $H = (W, F, w)$ 而言, $G 阅读全文

摘要： Chamberlain B. P., Shirobokov S., Rossi E., Frasca F., Markovich T., Hammerla N., Bronstein M. M. Hansmire M. Graph neural networks for link predictio 阅读全文