文献阅读总结：网络表示学习/图学习
本文是对网络表示学习/图学习(Network Representation Learning / Graph Learning)领域已读文献的归纳总结，长期更新。
朋友们，我们在github创建了一个图学习笔记库，总结了相关文章的论文、代码和我个人的中文笔记，能够帮助大家更加便捷地找到对应论文，欢迎star~
Chinese-Reading-Notes-of-Graph-Learning
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文献阅读总结合集
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文章目录
- 动态图
- 静态图
- 综述/理论/其他
动态图
文献阅读（一）WWW2018-Continuous-Time Dynamic Network Embeddings
文献阅读（二）KDD2018-Embedding Temporal Network via Neighborhood Formation
文献阅读（七）ICLR2019-DyREP：Learing Representations over Dynamic Graphs
文献阅读（11）ICLR2020-Inductive representation learning on temporal graphs
文献阅读（14）ACM2018-Learning dynamic embeddings from temporal interactions
文献阅读（18）NIPS2019-Self-attention with Functional Time Representation Learning
文献阅读（19）KDD2019-Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
文献阅读（20）IJCAI2019-Node Embedding over Temporal Graphs
文献阅读（22）IJCAI2019-Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graph
文献阅读（25）AAAI2020-EvolveGCN：Evolving Graph Convolutional Networks for Dynamic Graph
文献阅读（27）WSDM2020-DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
文献阅读（29）ICLR2020-Inductive and Unsupervised Representation Learning on Graph Structured Objects
文献阅读（38）ICLR2021-Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
文献阅读（46）CIKM2020-Continuous-Time Dynamic Graph Learning via Neural Interaction Processes
文献阅读（55）arXiv2021-Pre-Training on Dynamic Graph Neural Networks
文献阅读（71）WWW2022-CGC: Contrastive Graph Clustering for Community Detection and Tracking
文献阅读（72）WWW2022-TREND: TempoRal Event and Node Dynamics for Graph Representation Learning
文献阅读（74）KDD2022-ROLAND: Graph Learning Framework for Dynamic Graphs
文献阅读（75）ICML Workshop2020-ROLAND: Graph Learning Framework for Dynamic Graphs
静态图
文献阅读（四）ACL2017-CANE：Context-Aware Network Embedding for Relation Modeling
文献阅读（六）KDD2016-Asymmetric Transitivity Preserving Graph Embedding
文献阅读（八）KDD2018-Arbitrary-Order Proximity Preserved Network Embedding
文献阅读（九）KDD2014-DeepWalk：online learning of social representations
文献阅读（十）NIPS2017-Inductive representation learning on large graph
文献阅读（12）WWW2015-LINE：Large-scale Information Network Embedding
文献阅读（17）KDD2016-Structural Deep Network Embedding
文献阅读（24）KDD2020-GCC：Graph Contrastive Coding for Graph Neural Network Pre-Training
文献阅读（25）AAAI2020-EvolveGCN：Evolving Graph Convolutional Networks for Dynamic Graph
文献阅读（26）KDD2016-node2vec: Scalable Feature Learning for Networks
文献阅读（32）ACM TIST2017-PRIS：Profession Identification in Social Media
文献阅读（33）IJCAI2016-Max-Margin DeepWalk：Discriminative Learning of Network Representation
文献阅读（34）IJCAI2017-TransNet：Translation-Based NRL for Social Relation Extraction
文献阅读（35）TKDE2018-A Unified Framework for Community Detection and Network Representation Learning
文献阅读（36）WSDM2020-JNET: Learning User Representations via Joint Network Embedding and Topic Embedding
文献阅读（37）AAAI2021-Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
文献阅读（40）ICLR2021-Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
文献阅读（43）CIKM2017-Learning Community Embedding with Community Detection and Node Embedding on Graphs
文献阅读（44）ICLR2021-Accurate Learning of Graph Representations with Graph Multiset Pooling
文献阅读（45）AAAI2021-Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
文献阅读（51）SIGIR2021-Self-supervised Graph Learning for Recommendation
文献阅读（52）ICLR2021-Learnable Embedding Sizes for Recommender Systems
文献阅读（53）AAAI2021-Adversarial Directed Graph Embedding
文献阅读（56）AAAI2021-Graph Game Embedding
文献阅读（57）ICML Workshop2020-Deep Graph Contrastive Representation Learning
文献阅读（58）The Web Conf Workshop2021-Towards Robust Graph Contrastive Learning
文献阅读（65）NIPS2021-Towards Open-World Feature Extrapolation-An Inductive Graph Learning Approach
文献阅读（66）AAAI2021-Deep Fusion Clustering Network
文献阅读（67）TheWebConf2021-Structural Deep Clustering Network
文献阅读（68）ACL2021-Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis
文献阅读（69）NIPS2021-Do Transformers Really Perform Bad for Graph Representation
文献阅读（73）AAAI2022-SAIL: Self Augmented Graph Contrastive Learning
综述/理论/其他
文献阅读（五）CCF201803网络表示学习专题
文献阅读（13）AAAI2017-A Survey on Network Embedding
文献阅读（16）ICLR2020-On The Equivalence between Node Embeddings and Structural graph representations
文献阅读（28）IFIP2020-Explain Graph Neural Networks to Understand Weight Graph Features
文献阅读（30）WWW2019-GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
文献阅读（39）ICLR2021-How to Find Your Friendly Neighborhood：Graph Attention Design with Self-supervision
文献阅读（62）清华博士论文2018-面向社会计算的网络表示学习