├── GNN and Recommendation ├── README.md ├── Recommendation │ ├── A Neural Influence Diffusion Model for Social Recommendation │ │ ├── Diffnet.md │ │ └── img │ │ │ └── 1.png │ ├── A Simple Convolutional Generative Network for Next Item Recommendation │ │ ├── NextItNet.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation │ │ ├── EATNN.md │ │ └── img │ │ │ └── 1.png │ ├── Are Graph Augmentations Necessary Simple Graph Contrastive Learning for Recommendation │ │ └── SimGCL.md │ ├── Attention-Based Recommendation On Graphs │ │ ├── GARec.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Attentive Sequential Models of Latent Intent for Next Item Recommendation │ │ ├── ASLI.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── BERT4Rec- Sequential Recommendation with Bidirectional Encoder Representations from Transformer │ │ ├── BERT4Rec.md │ │ └── img │ │ │ └── 1.png │ ├── Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation │ │ ├── CIGAR.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer │ │ ├── TGSRec.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── DA-GCN A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation │ │ ├── DA-GCN.md │ │ └── img │ │ │ └── 1.png │ ├── Dynamic Graph Attention-Aware Networks for Session-Based Recommendation │ │ ├── DYAGNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Dynamic Graph Neural Networks for Sequential Recommendation │ │ ├── Dynamic Graph Neural Networks for Sequential Recommendation.md │ │ └── img │ │ │ ├── 5.jpg │ │ │ ├── DGRN-embdding.JPG │ │ │ ├── DGRN-squencelength.JPG │ │ │ ├── DGRNmodel.JPG │ │ │ ├── DGSR-2-3.JPG │ │ │ ├── DGSR-subgraph.JPG │ │ │ ├── DGSRcomponent.JPG │ │ │ ├── Loss.JPG │ │ │ ├── a1.jpg │ │ │ ├── algorithm.JPG │ │ │ ├── att.JPG │ │ │ ├── attcul.JPG │ │ │ ├── b1.jpg │ │ │ ├── h.JPG │ │ │ ├── itemnodeupdate.JPG │ │ │ ├── longterm.jpg │ │ │ ├── lt2.jpg │ │ │ ├── performance.JPG │ │ │ ├── sui.JPG │ │ │ ├── usernodeupdate.JPG │ │ │ └── yui.JPG │ ├── Dynamic Memory based Attention Network for Sequential Recommendation │ │ ├── DMAN.md │ │ └── img │ │ │ └── 1.png │ ├── Dynamic and Static Features-Aware Recommendation with Graph Neural Networks │ │ └── DSAGR.md │ ├── Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation │ │ ├── EGD-GNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Filter-enhanced MLP is All You Need for Sequential Recommendation │ │ ├── FeMLP-Rec.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 10.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ ├── 4.png │ │ │ ├── 5.jpg │ │ │ ├── 6.png │ │ │ ├── 7.png │ │ │ ├── 8.png │ │ │ └── 9.jpg │ ├── Global Context Enhanced Graph Neural Networks for Session-based Recommendation │ │ ├── GCE-GNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── Graph Contextualized Self-Attention Network for Session-based Recommendation │ │ ├── GC-SAN.md │ │ └── img │ │ │ └── 1.png │ ├── Graph Convolution Machine for Context-aware Recommender System │ │ ├── GCM.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters │ │ └── LCF.md │ ├── Graph Heterogeneous Multi-Relational Recommendation │ │ ├── GHCF.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Graph Neural Networks for Social Recommendation │ │ ├── GraphRec.md │ │ └── img │ │ │ └── 1.png │ ├── Graph Neural Networks with Dynamic and Static Representations for Social Recommendation │ │ ├── GNN-DSR.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Graph-based Embedding Smoothing for Sequential Recommendation │ │ ├── GES.md │ │ └── img │ │ │ └── 1.png │ ├── HAM- Hybrid Associations Models for Sequential Recommendation │ │ ├── HAM.md │ │ └── img │ │ │ └── 1.png │ ├── Handling Information Loss of Graph Neural Networks for Session-based Recommendation │ │ ├── LESSR.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── Hierarchical Gating Networks for Sequential Recommendation │ │ ├── HGN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Hyperbolic Hypergraphs for Sequential Recommendation │ │ ├── H2SeqRec.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ ├── Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks │ │ ├── Murzim.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.png │ ├── Item trend learning for sequential recommendation system using gated graph neural network │ │ ├── TRec.md │ │ └── img │ │ │ └── 1.png │ ├── KGAT Knowledge Graph Attention Network for Recommendation │ │ ├── KGAT.md │ │ └── img │ │ │ └── 1.png │ ├── Learning the Optimal Recommendation from Explorative Users │ │ └── Learning the Optimal Recommendation from Explorative Users.md │ ├── Leveraging Two Types of Global Graph for Sequential Fashion Recommendation │ │ ├── DGSR.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.jpg │ ├── Long- and Short-Term Self-Attention Network for Sequential Recommendation │ │ ├── LSSA.md │ │ └── img │ │ │ └── 1.png │ ├── Low-pass Graph Convolutional Network for Recommendation │ │ ├── LGCN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Memory Augmented Graph Neural Networks for Sequential Recommendation │ │ ├── MA-GNN.md │ │ └── img │ │ │ └── 1.png │ ├── Metapath and Entity-Aware Graph Neural Network for Recommendation │ │ ├── PEAGNN.md │ │ └── img │ │ │ └── 1.png │ ├── Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation │ │ ├── MKR.md │ │ └── img │ │ │ └── 1.png │ ├── Multi-behavior Recommendation with Graph Convolutional Networks │ │ ├── MBGCN.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ ├── Next-item Recommendation with Sequential Hypergraphs │ │ ├── HyperRec.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation │ │ ├── NOVA.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ ├── Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation │ │ ├── A-PGNN.md │ │ └── img │ │ │ └── 1.png │ ├── Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding │ │ ├── Caser.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations │ │ ├── PTGCN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── RetaGNN—Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation │ │ ├── RetaGNN—阅读笔记.md │ │ └── img │ │ │ ├── 3SR.png │ │ │ ├── H.jpg │ │ │ ├── SSA.PNG │ │ │ ├── loss.png │ │ │ └── retagnnliucheng.png │ ├── Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks │ │ ├── FGNN.md │ │ └── img │ │ │ └── 1.png │ ├── STAMP Short-Term AttentionMemory Priority Model for Session-based Recommendation │ │ ├── .~STAMP.md │ │ ├── STAMP.md │ │ └── img │ │ │ └── 1.png │ ├── Self-Attentive sequential Recommendation │ │ ├── SASRec.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.png │ ├── Self-supervised Graph Learning for Recommendation │ │ ├── SGL.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Sequential Recommendation for Cold-start Users with Meta Transitional Learning │ │ ├── MetaTL.md │ │ └── img │ │ │ └── 1.png │ ├── Sequential Recommendation with Graph Neural Networks │ │ ├── SURGE论文阅读笔记.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.png │ ├── Sequential Recommendation with Multiple Contrast Signals │ │ ├── ContraRec.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ ├── Sequential Recommendation with Self-Attentive Multi-Adversarial Network │ │ ├── MFGAN.md │ │ └── img │ │ │ └── 1.png │ ├── Session-based Recommendation with Graph Neural Networks │ │ ├── SR-GNN论文阅读笔记.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.jpg │ │ │ ├── 4.png │ │ │ ├── 5.png │ │ │ ├── 6.png │ │ │ ├── 7.png │ │ │ ├── 8.png │ │ │ ├── 9.png │ │ │ ├── ggnn.jpg │ │ │ └── sr-gnn.png │ ├── Session-based Social Recommendation via Dynamic Graph Attention Networks │ │ ├── DGRec.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.jpg │ ├── Sparse-Interest Network for Sequential Recommendation │ │ ├── SINE.md │ │ └── img │ │ │ └── 1.png │ ├── Star Graph Neural Networks for Session-based Recommendation │ │ ├── SGNN-HN.md │ │ └── img │ │ │ └── 1.png │ ├── TAGNN- Target Attentive Graph Neural Networks for Session-based Recommendation │ │ ├── TAGNN.md │ │ └── img │ │ │ └── 1.png │ ├── Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation │ │ ├── THIGE.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.png │ ├── Time Interval Aware Self-Attention for Sequential Recommendation │ │ ├── TiSASRec.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Time Matters- Sequential recommendation with complex temporal information │ │ ├── TASER.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Top-N personalized recommendation with graph neural networks in MOOCs │ │ ├── TP-GNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.jpg │ ├── UltraGCN Ultra Simplification of Graph Convolutional Networks for Recommendation │ │ ├── UltraGCN.md │ │ └── img │ │ │ └── 1.png │ └── xDeepFM Combining Explicit and Implicit Feature Interactions for Recommender Systems │ │ ├── img │ │ ├── 1.png │ │ └── 2.png │ │ └── xDeepFM.md ├── Survey │ ├── A survey on heterogeneous information network based recommender systems Concepts, methods, applications and resources │ │ └── HINRS survey.md │ ├── Bias and Debias in Recommender System-A Survey and Future Directions │ │ └── Bias and Debias in Recommender System-A Survey and Future Directions.md │ ├── Deep Learning on Graphs A Survey │ │ ├── DL on Graph.jpg │ │ ├── Deep Learning on Graphs A Survey.md │ │ ├── notations.jpg │ │ └── 图深度学习.xmind │ ├── Graph Learning Approaches to Recommender Systems A Review │ │ ├── GNN.jpg │ │ ├── Graph Learning Approaches to Recommender Systems A Review.pdf │ │ ├── Graph Learning Approaches to Recommender Systems A Review论文阅读笔记.md │ │ └── 基于推荐的图学习.xmind │ ├── Graph Neural Networks for Recommender Systems Challenges, Methods, and Directions │ │ ├── Graph Neural Networks for Recommender Systems Challenges, Methods, and Directions.md │ │ ├── Graph Neural Networks for Recommender Systems_Challenges,method,and directions.pdf │ │ └── img │ │ │ ├── BGCF-DGCN.jpg │ │ │ ├── GNN model.jpg │ │ │ ├── GNNsocial.JPG │ │ │ ├── The typical pipeline of recommender systems.jpg │ │ │ ├── bundle rs.jpg │ │ │ ├── cdrdetail.jpg │ │ │ ├── cdrmodel.jpg │ │ │ ├── cross-domain.jpg │ │ │ ├── diversity.jpg │ │ │ ├── drdetail.jpg │ │ │ ├── explaindetail.jpg │ │ │ ├── explainmodel.jpg │ │ │ ├── mbrdetail.jpg │ │ │ ├── mbrmodel.jpg │ │ │ ├── multi-behavior.jpg │ │ │ ├── objective.jpg │ │ │ ├── rankstage.jpg │ │ │ ├── sbrdetail.jpg │ │ │ ├── sbrmodel.jpg │ │ │ ├── scenario.jpg │ │ │ ├── sequence rs.jpg │ │ │ ├── sequencersmodel.JPG │ │ │ ├── session rs.jpg │ │ │ ├── social network.jpg │ │ │ ├── socialmodel.jpg │ │ │ ├── sqmodeldetail.JPG │ │ │ ├── stage.jpg │ │ │ └── stagemodel.jpg │ └── Graph Neural Networks in Recommendation Systems A survey │ │ ├── GNNRS.md │ │ └── img │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ └── 7.jpg ├── baseModel │ ├── A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models │ │ └── sample.md │ ├── A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering │ │ ├── RH-GCCF.md │ │ └── img │ │ │ └── 1.png │ ├── Attention Is All You Need │ │ ├── Transformer论文阅读笔记.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ ├── 4.png │ │ │ ├── 5.jpg │ │ │ ├── 6.png │ │ │ ├── 7.jpg │ │ │ ├── 8.png │ │ │ └── 9.png │ ├── Continuous-Time Dynamic Network Embeddings │ │ ├── CTDNE.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Disentangled Graph Collaborative Filtering │ │ ├── DGCF.md │ │ └── img │ │ │ └── 1.jpg │ ├── Dropedge Towards deep graph convolutional networks on node classification │ │ └── Dropedge.md │ ├── Dyrep Learning representations over dynamic graphs │ │ ├── Dyrep.md │ │ └── img │ │ │ └── 1.png │ ├── Fast and Deep Graph Neural Networks │ │ ├── FDGNN.md │ │ └── img │ │ │ └── 1.png │ ├── FastGCN Fast Learning with Graph Convolutional Networks via Importance Sampling │ │ └── FastGCN.md │ ├── Few-shot Link Prediction in Dynamic Networks │ │ ├── MetaDyGNN.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.jpg │ ├── Graph Attention Networks │ │ ├── GAT.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Graph Convolutional Matrix Completion │ │ ├── GCMC.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Graph4Rec- A Universal Toolkit with Graph Neural Networks for Recommender Systems │ │ ├── Graph4Rec.md │ │ └── img │ │ │ └── 1.png │ ├── Heterogeneous Graph Attention Network │ │ ├── HAN.md │ │ └── img │ │ │ └── 1.jpg │ ├── Heterogeneous Graph Neural Network │ │ ├── HetGNN.md │ │ └── img │ │ │ └── 1.png │ ├── Heterogeneous Graph Transformer │ │ ├── HGT.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ ├── 2.jpg │ │ │ ├── 3.png │ │ │ └── 4.jpg │ ├── Hypergraph Neural Networks │ │ ├── HGNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── Inductive Representation Learning on Large Graphs │ │ ├── GraphSAGE.md │ │ └── img │ │ │ └── gsal.jpg │ ├── Inductive Representation Learning on Temporal Graphs │ │ ├── TGAT.md │ │ └── img │ │ │ ├── 1.jpg │ │ │ └── 2.jpg │ ├── LightGCN │ │ ├── Simplifying and Powering Graph Convolution Network for Recommendation论文阅读笔记.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ ├── MixGCF An Improved Training Method for Graph Neural Network-based Recommender Systems │ │ ├── MixGCF.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── Neural Graph Collaborative Filtering │ │ ├── NGCF.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── Policy-GNN Aggregation Optimization for Graph Neural Networks │ │ ├── Policy-GNN.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ └── Semi-Supervised Classification with Graph Convolutional Networks │ │ ├── GCN总结.md │ │ ├── Semi-Supervised Classification with Graph Convolutional Networks论文阅读笔记.md │ │ └── img │ │ ├── compare model.jpg │ │ ├── dataset.JPG │ │ ├── gcnmodel.jpg │ │ ├── l2note.jpg │ │ ├── l3note.jpg │ │ ├── l4note.jpg │ │ ├── latex1.jpg │ │ └── summary.JPG └── 图神经网络基础算法 │ ├── 0.GraphEmbedding │ ├── 0.Word2Vec和Embedding.md │ ├── 1.Deepwalk.md │ ├── 2.LINE.md │ ├── 3.node2vec.md │ ├── 4.Struc2vec.md │ ├── 5.SDNE.md │ ├── 6.GrephEmbedding算法总结.md │ └── img │ │ ├── deepwalk.jpg │ │ ├── deepwalk.png │ │ ├── gezong.jpg │ │ ├── kl.png │ │ ├── kl2.png │ │ ├── kld.png │ │ ├── kleg.png │ │ ├── lhfb.png │ │ ├── lhfbeg.png │ │ ├── line.png │ │ ├── lineeg.png │ │ ├── linexiaog.png │ │ ├── n2e.png │ │ ├── n2improve.png │ │ ├── n2r2.png │ │ ├── nodeal.png │ │ ├── nv1.jpg │ │ ├── pihat.jpg │ │ ├── s2v.png │ │ ├── s2v1.png │ │ ├── s2vdytime.png │ │ ├── s2vr.png │ │ ├── s2vw1.png │ │ ├── s2vw2.png │ │ ├── sdne1.png │ │ ├── w1.png │ │ └── word2vec.jpg │ ├── 1.GCN,GAT,GraphSAGE │ ├── 1.GCN.md │ ├── 2.0.GraphSAGE.md │ ├── 2.1.GraphSAGE minibatch.md │ ├── 2.2.GraphSAGE embedding.md │ ├── 3.GAT.md │ ├── 4.图网络应用.md │ └── img │ │ ├── GCN.png │ │ ├── GCN1.png │ │ ├── GCNOP1.png │ │ ├── GCNOP2.png │ │ ├── concat.PNG │ │ ├── gat.png │ │ ├── gat1.png │ │ ├── gat2.png │ │ ├── gat3.png │ │ ├── gatr.png │ │ ├── gcn2.png │ │ ├── gcn3.png │ │ ├── gcn4.png │ │ ├── gnr1.png │ │ ├── gs6.png │ │ ├── gsag.png │ │ ├── gsal.jpg │ │ ├── gse.png │ │ ├── gseg.jpg │ │ ├── gseg.png │ │ ├── gsmini.png │ │ ├── gsmneg.png │ │ └── gsmneg2.png │ ├── 2.PyG库使用 │ ├── 1.PYG库的应用.md │ ├── 2.PyG构建GCNGATGraphSAGE.md │ └── img │ │ └── pyg1.png │ ├── 3.0.图网络分类 │ ├── 3.0图网络分类.md │ └── img │ │ ├── gae.jpg │ │ ├── gae1.png │ │ ├── gn1.png │ │ ├── gn2.png │ │ ├── gn3.png │ │ ├── gn4.png │ │ ├── gn5.png │ │ ├── gn6.png │ │ ├── gn7.png │ │ ├── mpnn1.png │ │ ├── mpnn2.png │ │ ├── nlnn1.png │ │ ├── nlnn2.png │ │ ├── patchsan.jpg │ │ ├── patchsan2.png │ │ ├── rgnn.png │ │ └── rgnn2.png │ ├── 3.1.异构图网络算法 │ ├── 1.HAN算法理论.md │ ├── 2.GTN算法理论.md │ ├── 3.metapath2vec算法理论.md │ ├── 4.GATNE算法理论.md │ └── img │ │ ├── HAN.png │ │ ├── HAN2.png │ │ ├── HAN3.png │ │ ├── HAN4.png │ │ ├── HAN5.png │ │ ├── HAN6.jpg │ │ ├── HANsa1.png │ │ ├── Hansa2.jpg │ │ ├── hanclu.jpg │ │ ├── hansa3.png │ │ └── hansa4.png │ ├── 3.2.二部图网络算法 │ └── 1.BINE算法理论.md │ ├── 3.3.符号图网络算法 │ ├── 1.SGCN算法理论.md │ ├── 2.SiGAT算法理论.md │ └── 3.SDGNN算法理论.md │ ├── 3.4.动态图网络算法 │ ├── 0.动态图的分类.md │ ├── 1.1.DySAT算法原理.md │ ├── 1.2.DySAT算法实现.md │ ├── 2.1.EvolveGCN算法原理.md │ ├── 2.2.EvolveGCN算法实现.md │ ├── 3.1.StreamingGNN算法原理.md │ ├── 3.2.StreamingGNN算法实现.md │ ├── 4.1.TGAT算法原理.md │ ├── 4.2.TGAT算法实现.md │ └── img │ │ ├── dy1.png │ │ ├── dysat1.png │ │ ├── dysat2.png │ │ ├── dysat3.png │ │ ├── dysat4.jpg │ │ ├── dysat5.png │ │ ├── dysat6.png │ │ ├── dysat7.png │ │ ├── dysate1.png │ │ ├── dysate2.png │ │ ├── dysate3.png │ │ ├── dysate4.png │ │ ├── dysate5.png │ │ ├── egcn1.png │ │ ├── egcn10.png │ │ ├── egcn2.png │ │ ├── egcn3.png │ │ ├── egcn4.png │ │ ├── egcn5.png │ │ ├── egcn6.png │ │ ├── egcn7.png │ │ ├── egcn8.png │ │ ├── egcn9.png │ │ ├── sgnn1.png │ │ ├── sgnn10.png │ │ ├── sgnn11.png │ │ ├── sgnn12.png │ │ ├── sgnn13.png │ │ ├── sgnn14.png │ │ ├── sgnn15.png │ │ ├── sgnn2.jpg │ │ ├── sgnn3.png │ │ ├── sgnn4.png │ │ ├── sgnn5.png │ │ ├── sgnn6.png │ │ ├── sgnn7.png │ │ ├── sgnn8.jpg │ │ ├── sgnn9.png │ │ ├── tgat1.jpg │ │ ├── tgat2.png │ │ ├── tgat3.png │ │ ├── tgat4.png │ │ ├── tgat5.png │ │ ├── tgat6.png │ │ ├── tgat7.png │ │ └── tgat8.jpg │ └── 3.5.超图网络算法 │ ├── 1.HGNN算法理论.md │ └── 2.DHGNN算法理论.md ├── README.md ├── conda使用 ├── conda navigator无法打开.md ├── pytorch+jupyter.md ├── pytorch初始化参数可能存在的问题.md ├── 安装py3.6+TensorFlow1.13.1.md ├── 安装py3.6+torch1.4.0.md ├── 安装pyg脚本.md └── 重命名虚拟环境.md ├── paper-tools ├── README.md ├── 如何定义或寻找研究的问题或对象.md └── 机器学习九种技术工具.md ├── 动手学深度学习pytorch ├── 1.数据操作 │ ├── 1.数据操作.md │ └── 2.数据预处理.md ├── 2.线性代数相关 │ ├── 1.线性代数.md │ ├── 2.矩阵计算.md │ ├── 3.自动求导.md │ └── img │ │ ├── 1646729002(1).jpg │ │ ├── 1646729197(1).png │ │ ├── 1646729249(1).png │ │ ├── 1646729290(1).png │ │ ├── 1646729530(1).png │ │ ├── 7..2.png │ │ ├── 7.1.png │ │ ├── 7.3.png │ │ ├── 7.4.png │ │ ├── 7.5.png │ │ ├── 7.6.png │ │ ├── 7.7.png │ │ ├── 7.jpg │ │ ├── tidu.png │ │ └── tidu2.png ├── 3.线性神经网络 │ ├── 1.线性回归 │ │ ├── 1.线性回归+基础优化算法.md │ │ ├── 2.从零实现线性回归.md │ │ ├── 3.线性回归简洁实现.md │ │ └── img │ │ │ ├── 8.1.png │ │ │ ├── 8.2.png │ │ │ ├── 8.3.png │ │ │ └── 8.4.png │ └── 2.sotfmax回归 │ │ ├── 0.softmax回归.md │ │ ├── 1.损失函数.md │ │ ├── 2.图像分类数据集.md │ │ ├── 3.softmax回归实现.md │ │ ├── 4.softmax简洁实现.md │ │ └── img │ │ ├── 9.1.png │ │ ├── 9.2.png │ │ ├── 9.3.png │ │ ├── 9.4.png │ │ ├── 9.5.png │ │ ├── 9.6.png │ │ └── 9.7.png ├── 4.多层感知机 │ ├── 1.感知机与多层感知机 │ │ ├── 0.感知机.md │ │ ├── 1.多层感知机.md │ │ ├── 2.多层感知机从零实现.md │ │ ├── 3.多层感知机简洁实现.md │ │ └── img │ │ │ ├── 10.1.png │ │ │ ├── 10.2.png │ │ │ ├── 10.3.png │ │ │ ├── 10.4.png │ │ │ ├── 10.5.png │ │ │ ├── 10.6.jpg │ │ │ ├── 10.7.png │ │ │ ├── 10.8.png │ │ │ └── 10.9.jpg │ ├── 2.过拟合、欠拟合 │ │ ├── 1.模型选择、欠拟合和过拟合.md │ │ ├── 2.过拟合欠拟合代码实现.md │ │ └── img │ │ │ ├── 11.1.png │ │ │ ├── 11.2.jpg │ │ │ ├── 11.3.png │ │ │ ├── 11.4.png │ │ │ ├── 11.5.png │ │ │ ├── 11.6.png │ │ │ ├── 11.7.png │ │ │ └── 11.8.png │ ├── 3.权重衰减 │ │ ├── 1.权重衰退.md │ │ ├── 2.权重衰退实现.md │ │ └── img │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ └── 3.png │ ├── 4.丢弃法(dropout) │ │ ├── dropout.md │ │ ├── dropout实现.md │ │ └── img │ │ │ ├── 1.png │ │ │ └── 2.png │ └── 5.数值稳定性、模型初始化、激活函数 │ │ ├── 1.数值稳定性.md │ │ └── img │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ └── 8.png ├── 5.神经网络基础 │ ├── 1.模型构造.md │ ├── 2.参数管理.md │ ├── 3.自定义层.md │ ├── 4.读写文件.md │ └── img │ │ └── 10.1.png ├── 6.0卷积层 │ ├── 1.从全连接到卷积.md │ ├── 2.1.卷积层代码实现.md │ ├── 2.卷积层.md │ ├── 3.1.填充和步幅代码.md │ ├── 3.填充和步幅.md │ ├── 4.1.多输出输入代码实现.md │ ├── 4.多输入多输出通道.md │ ├── 5.1池化层代码实现.md │ ├── 5.池化层.md │ └── img │ │ ├── 11.1.png │ │ ├── 11.10.png │ │ ├── 11.11.png │ │ ├── 11.12.png │ │ ├── 11.13.png │ │ ├── 11.2.png │ │ ├── 11.3.png │ │ ├── 11.4.png │ │ ├── 11.5.png │ │ ├── 11.6.png │ │ ├── 11.7.png │ │ ├── 11.8.png │ │ └── 11.9.png ├── 6.1.卷积神经网络模型 │ ├── 1.LeNet.md │ ├── 2.1.AlexNet代码实现.md │ ├── 2.AlexNet.md │ ├── 3.1.GoogleNet代码实现.md │ ├── 3.GoogleLeNet-Inception v3.md │ ├── 4.1.BatchNorm代码实现.md │ ├── 4.批量规范化-Batch Norm.md │ ├── 5.1.ResNet代码实现.md │ ├── 5.残差网络Resnet.md │ └── img │ │ ├── 12.1.png │ │ ├── 12.10.png │ │ ├── 12.11.png │ │ ├── 12.12.png │ │ ├── 12.13.png │ │ ├── 12.14.png │ │ ├── 12.15.png │ │ ├── 12.16.png │ │ ├── 12.2.png │ │ ├── 12.3.png │ │ ├── 12.4.png │ │ ├── 12.5.png │ │ ├── 12.6.png │ │ ├── 12.7.png │ │ ├── 12.8.png │ │ └── 12.9.png ├── 7.0.循环神经网络 │ ├── 1.0.序列模型.md │ ├── 1.1序列模型代码实现.md │ ├── 2.文本预处理.md │ ├── 3.0语言模型和数据集.md │ ├── 3.1语言模型的代码实现.md │ ├── 4.0循环神经网络.md │ ├── 4.1循环神经网络从零实现.md │ ├── 4.2循环神经网络简洁实现.md │ └── img │ │ ├── 13.1.png │ │ ├── 13.10.png │ │ ├── 13.11.png │ │ ├── 13.12.png │ │ ├── 13.13.png │ │ ├── 13.14.png │ │ ├── 13.15.png │ │ ├── 13.16.jpg │ │ ├── 13.17.png │ │ ├── 13.18.png │ │ ├── 13.2.png │ │ ├── 13.3.png │ │ ├── 13.4.png │ │ ├── 13.5.png │ │ ├── 13.6.png │ │ ├── 13.7.png │ │ ├── 13.8.png │ │ └── 13.9.png ├── 7.1.循环神经网络模型 │ ├── 1.1.GRU代码实现.md │ ├── 1.门控循环单元 GRU.md │ ├── 2.1.LSTM代码实现.md │ ├── 2.长短期记忆网络LSTM.md │ ├── 3.双向循环神经网络.md │ ├── 4.seq2seq.md │ ├── 5.束搜索.md │ └── img │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.jpg │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── 8.0.注意力机制 │ ├── 1.注意力机制.md │ ├── 2.注意力汇聚：Nadaraya-Watson 核回归.md │ ├── 3.0.注意力分数.md │ ├── 3.1.注意力分数代码实现.md │ ├── 4.0使用注意机制的seq2seq.md │ ├── 5.自注意力.md │ ├── 6.0.Transformer.md │ ├── 6.1.Transformer代码.md │ └── img │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.jpg │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png └── 9.优化算法 │ ├── 1.凹凸性.md │ ├── 2.梯度下降.md │ ├── 3.冲量法(momentum).md │ ├── 4.Adam.md │ └── img │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png └── 实用机器学习 ├── 1.2数据获取.md ├── 1.3网页数据抓取.md ├── 1.4数据标注.md ├── 2.1探索性数据分析.md ├── 2.2数据清理.md ├── 2.3数据变换.md ├── 2.4特征工程.md ├── 2.5数据部分总结.md ├── 3.1机器学习介绍.md ├── 3.2决策树模型.md ├── 3.3线性模型.md ├── 3.4随机梯度下降.md ├── 3.5多层感知机.md ├── 3.6卷积神经网络.md ├── 3.7循环神经网络.md ├── 4.1模型评估.md ├── 4.2过拟合和欠拟合.md ├── 4.3模型验证.md ├── 5.1偏差和方差.md ├── 5.2Bagging.md ├── 5.3Boosting.md ├── 5.4Stacking.md ├── 9.1模型调参.md └── img ├── 11.png ├── ROC.jpg ├── ROC2.jpg ├── bagging.png ├── 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