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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Xu Y., Zhao S., Song J., Stewart R. and Ermon S. A theory of usable information under computational constraints. International Conference on Learning 阅读全文
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Wang L. and Joachims T. Uncertainty quantification for fairness in two-stage recommender systems. In International World Wide Web Conference (WWW), 20 阅读全文
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Zhang Y., Dong X., Ding W., Li B., Jiang P. and Gai K. Divide and Conquer: Towards better embedding-based retrieval for recommender systems from a mul 阅读全文
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Gao Z., Guo J., Tan X., Zhu Y., Zhang F., Bian J. and Xu L. Difformer: Empowering diffusion models on the embedding space for text generation. arXiv p 阅读全文