摘要：
目录概Noise contrastive estimation Mnih A. and Teh Y. W. A fast and simple algorithm for training neural probabilistic language models. ICML, 2012. 概 NCE 阅读全文
摘要：
目录概InstructRecInstruction Generation Zhang J., Xie R., Hou Y., Zhao W. X., Lin L., Wen J. Recommendation as instruction following: a large language mo 阅读全文
摘要：
目录概符号说明DeepWalk代码 Perozzi B., AI-Rfou R. and Skiena S. DeepWalk: Online learning of social representations. KDD, 2014. 概 经典的 graph embedding 学习方法. 符号说 阅读全文
摘要：
目录概符号说明MotivationFavardGNN代码 Guo Y. and Wei Z. Graph neural networks with learnable and optimal polynomial bases. ICML, 2023. 概 自动学多项式基的谱图神经网络. 符号说明 \ 阅读全文
摘要：
目录概符号说明MotivationNewtonNet代码 Xu J., Dai E., Luo D>, Zhang X. and Wang S. Learning graph filters for spectral gnns via newton interpolation. 2023. 概 令谱 阅读全文
摘要：
目录概符号说明DSF代码 Guo J., Huang K, Yi X. and Zhang R. Graph neural networks with diverse spectral filtering. WWW, 2023. 概 为每个结点赋予不同的多项式系数. 符号说明 \(\mathcal{ 阅读全文
摘要：
目录概符号说明MotivationChebNetII代码 He M., Wei Z. and Wen J. Convolutional neural networks on graphs with chebyshev approximation, revisited. NIPS, 2022. 概 作 阅读全文
摘要：
[TOC] Guo J., Du L, Chen X., Ma X., Fu Q., Han S., Zhang D. and Zhang Y. On manipulating signals of user-item graph: A jacobi polynomial-based graph c 阅读全文
摘要：
目录概符号说明Spectral GNNChoice of Basis for Polynomial FiltersJacobiConv代码 Wang X. and Zhang M. How powerful are spectral graph neural networks? ICML, 2022 阅读全文
摘要：
目录概ListNetPermutation ProbabilityTop-k ProbabilityListMLE Cao Z., Qin T., Liu T., Tsai M. and Li H. Learning to rank: from pairwise approach to listwi 阅读全文
摘要：
目录概符号说明GATv2代码 Brody S., Alon U. and Yahav E. How attentive are graph attention networks? ICLR, 2022. 概 作者发现了 GAT 的 attention 并不能够抓住边的重要性, 于是提出了 GATv2 阅读全文
摘要：
目录概符号说明Shadow-GNN代码 Zeng H., Zhang M., Xia Y., Srivastava A., Malevich A., Kannan R., Prasanna V., Jin L. and Chen R. Decoupling the depth and scope o 阅读全文
摘要：
目录概TallRec代码 Bao K., Zhang J., Zhang Y., Wang W., Feng F. and He X. TALLRec: An effective and efficient tuning framework to align large language model 阅读全文
摘要：
目录概MotivationMarked Temporal Point Process代码 Du N., Dai H., Trivedi R., Upadhyay U., Gomez-Rodriguze M. and Song L. Recurrent marked temporal point pr 阅读全文
摘要：
目录TPPEvolutionary point processesConditional intensity function [\(t\)]Conditional intensity function [\(t, \kappa\)]InferenceSimulationInverse Method 阅读全文
摘要：
目录概符号说明LLP代码 Guo Z., Shiao W., Zhang S., Liu Y., Chawla N. V., Shah N. and Zhao T. Linkless link prediction via relational distillation. ICML, 2023. 概 阅读全文
摘要：
目录概符号说明DistillGCNLocal Structure Preserving代码 Yang Y., Qiu J., Song M., Tao D. and Wang X. Distilling knowledge from graph convolutional networks. CVP 阅读全文
摘要：
目录概符号说明DKD代码 Zhao B., Cui Q., Song R., Qiu Y. and Liang J. Decoupled knowledge distillation. CVPR, 2022. 概 对普通的 KD (Knowledge Distillation) 损失解耦得到 Tar 阅读全文
摘要：
目录概符号说明AirGNN代码 Liu X., Ding J., Jin W., Xu H., Ma Y., Liu Z. and Tang J. Graph neural networks with adaptive residual. NIPS, 2021. 概 基于 UGNN 框架的一个更加鲁 阅读全文
摘要：
目录概 Ma Y., Liu X., Shah N. and Tang J. Is homophily a necessity for graph neural networks? ICLR, 2022. 概 探究 Homophily 假设 (即相互连接的结点相似) 对于 GCN 发挥效果是否是必须 阅读全文