一、翻译距离模型：
【TransE】 Translating Embeddings for Modeling Multi-relational Data 全文链接：
文献全文 - 学术范 (xueshufan.com)【这个网站上边大部分都有，链接太多似乎不能过审，只能辛苦大家自己复制粘贴搜一下，并且还可以一键翻译摘要了解哦！】
【TransH】 Knowledge Graph Embedding by Translating on Hyperplanes
【TransR】 Learning Entity and Relation Embeddings for Knowledge Graph Completion
【TransD】 Knowledge Graph Embedding via Dynamic Mapping Matrix
【TranSparse】 Knowledge graph completion with adaptive sparse transfer matrix
【TransM】 Transition-based knowledge graph embedding with relational mapping properties
【MianfoldE】 From one point to a manifold Knowledge graph embedding for precise link prediction
【TransF】 Knowledge graph embedding by flexible translation
【TransA】 TransA: An adaptive approach for knowledge graph embedding
二、语义匹配模型
【RESCAL】 A three-way model for collective learning on multi-relational data
【DistMult】 Embedding entities and relations for learning and inference in knowledge bases
【HoLE】 Holographic embeddings of knowledge graphs
【ComplEx】 Complex embeddings for simple link prediction
三、随机游走模型
【DeepWalk】 Online Learning of Social Representations
【LINE】 Large-scale Information Network Embedding
【node2vec】 Scalable Feature Learning for Networks
四、子图汇聚模型
【GCN】 Semi-Supervised Classification with Graph Convolutional Networks
【GAT】 Graph Attention Networks
【GraphSage】 Inductive Representation Learning on Large Graphs
五、多关系表征模型
【RGCN】 Modeling Relational Data with Graph Convolutional networks [ESWC 2018]
【WGCN】 End-to-end structure-aware convolutional networks for knowledge base completion [AAAI 2019]
【TransGCN】 Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction [K-CAP 2019]
【CompGCN】 Composition-based Multi-Relational Graph Convolutional Networks [ICLR 2020]
【HGT】 Heterogeneous Graph Transformer [WWW 2020]
【HGAN】 Heterogeneous Graph Attention Network [WWW 20219]
六、综述类
A Comprehensive Survey on Graph Neural Networks全文链接：文献全文 - 学术范 (xueshufan.com)
Knowledge Graph Embedding A Survey of Approaches and Applications 文献全文 - 学术范 (xueshufan.com)
Representation Learning on Graphs Methods and Applications 全文链接：文献全文 - 学术范 (xueshufan.com)
七、深度学习系列25篇
▌必读系列4篇|附详细解读
【GCNII】Simple and Deep Graph Convolutional Networks [ICML 2020]
【GRAND】Graph Random Neural Networks for Semi-Supervised Learning on Graphs [NeurIPS 2020]
【DAGNN】Towards Deeper Graph Neural Networks [KDD 2020]
【APPNP】Predict then Propagate: Graph Neural Networks meet Personalized PageRank [ICLR 2019]
▌Guohao Li系列3篇
【Guohao Li】DeepGCNs: Can GCNs Go as Deep as CNNs? [ICCV 2019]
【Guohao Li】DeeperGCN: All You Need to Train Deeper GCNs [arXiv 2020]
【Guohao Li】Training Graph Neural Networks with 1000 Layers [ICML 2021]
▌2021年最新4篇推荐
Adaptive Universal Generalized PageRank Graph Neural Network [ICLR 2021]
Graph Neural Networks Inspired by Classical Iterative Algorithms [ICML 2021]
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models [ICLR 2021]
Adaptive Universal Generalized PageRank Graph Neural Network [ICLR 2021]
▌2020年10篇推荐
【DropEdge】Towards Deep Graph Convolutional Networks on Node Classification [ICLR 2020]
【PairNorm】Tackling Oversmoothing in GNNs [ICLR 2020]
Towards Deeper Graph Neural Networks with Differentiable Group Normalization [NeurIPS 2020]
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks [NeurIPS 2020]
Bayesian Graph Neural Networks with Adaptive Connection Sampling [ICML 2020]
Continuous Graph Neural Networks [ICML 2020]
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification [ICLR 2020]
Measuring and Improving the Use of Graph Information in Graph Neural Networks [ICLR 2020]
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View [AAAI 2020]
【JK-Net】Representation Learning on Graphs with Jumping Knowledge Networks [ICML 2018]
▌其他4篇
Deep Graph Neural Networks with Shallow Subgraph Samplers [arXiv 2020]
Tackling Over-Smoothing for General Graph Convolutional Networks [arXiv 2020]
Effective Training Strategies for Deep Graph Neural Networks [arXiv 2020]
Revisiting Over-smoothing in Deep GCNs [arXiv 2020]
整理不易，欢迎来学术范找我玩儿~
