馒头and花卷

摘要： 目录概符号说明MMGCN代码 Wei Y., Wang X., Nie L., He X., Hong R. and Chua T. MMGCN: Multi-modal graph convolution network for personalized recommendation of mic 阅读全文

摘要： 目录概符号说明GenAgg代码 Kortvelesy R., Morad S. and Prorok A. Generalised f-mean aggregation for graph neural networks. NIPS, 2023. 概 基于 MPNN 架构的 GNN 主要在于 agg 阅读全文

摘要： 目录概符号说明Cold Brew代码 Zheng W., Huang E. W., Rao N., Katariya S., Wang Z., Subbian K. Cold brew: Distilling graph node representations with incomplete or 阅读全文

摘要： 目录概符号说明MotivationGCOND代码 Jin W., Zhao L., Zhang S., Liu Y., Tang J. and Shah N. Graph condensation for graph neural networks. ICLR, 2022. 概 图上做压缩的工作. 阅读全文

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