长期更新-好论文解读收藏

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Social Diffusion

Title Unscramble
Social Influence Locality for Modeling Retweeting Behavior 解读 代码
Role-Aware Conformity Influence Modeling and Analysis in Social Network 解读
DeepInf:Social Influence Prediction with Deep Learning 解读
Reverse Influence Sampling in Python 解读及代码
Cost-effective Outbreak Detection in Networks(CELF) 解读及代码 解读
DeepCas: an End-to-end Predictor of Information Cascades 解读
Inf2vec: Latent Representation Model for Social Influence Embedding 解读
POI2Vec: Geographical Latent Representation for Predicting Future Visitors 解读

Network Embedding and GNN

Title Unscramble
Heterogeneous Graph Attention Network 解读
Attributed Social Network Embedding 解读
Self-Translation Network Embedding 解读1 解读2
Self-Paced Network Embedding 解读
CANE: Context-Aware Network Embedding for Relation Modeling 解读
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning 解读1 解读2
Embedding Temporal Network via Neighborhood Formation 解读
Network Representation Learning with Rich Text Information 解读1 解读2
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks 解读
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction 解读
Context-Aware Network Embedding for Relation Modeling 解读
GAN在网络特征学习中的应用 this
一文读懂「Attention is All You Need」 附代码实现 解读
node2vec: Scalable Feature Learning for Networks 解读1 解读2 解读3
PTE:Predictive Text Embedding through Large-scale 解读
struc2vec: Learning Node Representations from Structural Identity 解读
AspEm: Embedding Learning by Aspects in HINs 解读
Max-Margin DeepWalk: Discriminative Learning of Network Representation 解读1 解读2
Structural Deep Network Embedding 解读
Learning Structural Node Embeddings via Diffusion Wavelets 解读
HARP: Hierarchical Representation Learning for Networks 解读
Dynamic Network Embedding by Modeling Triadic Closure Process 解读
RaRE: Social Rank Regulated Large-scale Network Embedding 解读
TransNet: Translation-Based Network Representation Learning for Social Relation Extraction 解读
GraphGAN: Graph Representation Learning with Generative Adversarial Nets 解读
Graph Convolutional Network 解答
GraRep: Learning Graph Representations with Global Structural Information 解读
Deep Dynamic Network Embedding for Link Prediction 解读
Representation Learning for Attributed Multiplex Heterogeneous Network GATNE
Inductive Representation Learning on Large Graphs GraphSAGE
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba 解读
Graph Convolutional Neural Networks for Web-Scale Recommender Systems PinSAGE
Semi-Supervised Classification with Graph Convolutional Networks Semi-GCN
Joint Type Inference on Entities and Relations via Graph Convolutional Networks 解读
图卷积神经网络(GCN) GCN
Adaptive SamplingTowards Fast Graph Representation Learning 解读
ProNE: Fast and Scalable Network Representation Learning ProNE
ROTATE:Knowledge graph embedding by relational rotate in complex space Rotate
Signed Graph Convolutional Network SGCN
GAT: Graph Attention Network GAT GAT2
Large-Scale Learnable Graph Convolutional Networks LGCN LGCN2 LGCN3 LGCN4 LGCN5 代码分析
Hierarchical Graph Representation Learning with Differentiable Pooling DiffPool DiffPool

ML & DL

Title Unscramble
常见散度与距离(KL散度,JS散度,Wasserstein距离,互信息MI) Here
【简化数据】奇异值分解(SVD) Here
AUC的计算与近似 Here
PCA Here Here Here
AdaBoost Here Here
Batch Normalization Here
矩阵的正定及半正定 Here
精确率、召回率、F1 值、ROC、AUC Here
Hierarchical Softmax Here
傅立叶变换 Here
卷积神经网络(CNN)之一维卷积、二维卷积、三维卷积详解 Here
变分自编码器(VAE) Here

工具

Title Unscramble
tf.slice() Here
tf.tile() Here
TensorFlow中global_step的简单分析 Here
tensorflow之tf.nn.l2_normalize与l2_loss的计算 Here
tf.nn.top_k() Here
python中matplotlib的颜色及线条控制 Here
python set(集合) & 与 and 与 or之间的区别 Here
numpy实现top_k Here
np reshape(-1,1) Here
傅里叶变换 Here Here Here

技术博客

https://davidham3.github.io/blog

https://fenghz.github.io/index.html

https://archwalker.github.io/

感谢支持!