• ijcai2018

• Co-Regularized Deep Multi-Network Embedding
• Dynamic Embeddings for Language Evolution
• On Exploring Semantic Meanings of Links for Embedding Social Networks
• RaRE：Social Rank Regulated Large-scale Network Embedding
• SIDE：Representation Learning in Signed Directed Networks
• VERSE：Versatile Graph Embeddings from Similarity Measures

• Improving Knowledge Graph Embedding Using Simple Constraints
• Domain Adaptation with Adversarial Training and Graph Embeddings
• Towards Understanding Geometry of Knowledge Graph Embeddings

1. Representation Learning on Graphs: Methods and Applications. William L. Hamilton, Rex Ying, Jure Leskovec. 2017. paper
2. Graph Embedding Techniques, Applications, and Performance: A Survey. Palash Goyal, Emilio Ferrara. 2017. paper
3. A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications. Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang. 2017. paper
4. Network Representation Learning: A Survey. Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. 2018. paper
5. Network Representation Learning: An Overview.(In Chinese) Cunchao Tu, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2017. paper
6. Relational inductive biases, deep learning, and graph networks. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu. 2018. paper

1. DeepWalk: Online Learning of Social Representations. Bryan Perozzi, Rami Al-Rfou, Steven Skiena. KDD 2014. paper
2. node2vec: Scalable Feature Learning for Networks. Aditya Grover, Jure Leskovec. KDD 2016. paper code
3. Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks. Yann Jacob, Ludovic Denoyer, Patrick Gallinar. WSDM 2014. paper
4. GraRep: Learning Graph Representations with Global Structural Information. *Shaosheng Cao, Wei Lu, Qiongkai Xu.*CIKM 2015. paper code
5. LINE: Large-scale Information Network Embedding. Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Me. WWW 2015. paper code
6. Network Representation Learning with Rich Text Information. Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Y. Chang. IJCAI 2015. paper
7. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. *Jian Tang, Meng Qu, Qiaozhu Mei.*KDD 2015. paper
8. Heterogeneous Network Embedding via Deep Architectures. Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang. KDD 2015. paper
9. Deep Neural Networks for Learning Graph Representations. Shaosheng Cao, Wei Lu, Xiongkai Xu. AAAI 2016. paper
10. Revisiting Semi-supervised Learning with Graph Embeddings. *Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov.*ICML 2016. paper
11. Tri-Party Deep Network Representation. Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, Yang Wang. IJCAI 2016. paper
12. Discriminative Deep RandomWalk for Network Classification. Juzheng Li, Jun Zhu, Bo Zhang. ACL 2016. paper
13. Structural Deep Network Embedding. Daixin Wang, Peng Cui, Wenwu Zhu. KDD 2016. paper
14. Semi-supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017. paper
15. Fast Network Embedding Enhancement via High Order Proximity Approximation. Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu. IJCAI 2017. paper code
16. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami. KDD 2017. paper code
17. Learning from Labeled and Unlabeled Vertices in Networks. Wei Ye, Linfei Zhou, Dominik Mautz, Claudia Plant, Christian Böhm. KDD 2017. paper
18. struc2vec: Learning Node Representations from Structural Identity. Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo. KDD 2017. paper code
19. An Attention-based Collaboration Framework for Multi-View Network Representation Learning. Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han. CIKM 2017. paper
20. On Embedding Uncertain Graphs. Jiafeng Hu, Reynold Cheng, Zhipeng Huang, Yixang Fang, Siqiang Luo. CIKM 2017. paper
21. Multi-view Clustering with Graph Embedding for Connectome Analysis. Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S Yu, Alex D Leow, Ann B Ragin. CIKM 2017. paper
22. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang. WSDM 2018. paper
23. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu. WSDM 2018. paper
24. Adversarial Network Embedding. Quanyu Dai, Qiang Li, Jian Tang, Dan Wang. AAAI 2018. paper

awesome-network-embedding

awesome-embedding-models

Must-read papers on network representation learning (NRL) / network embedding (NE)

Must-read papers on knowledge representation learning (KRL) / knowledge embedding (KE)

Network Embedding Resources

awesome-embedding-models

2vec-type embedding models

Stanford Network Analysis Project

Deep Dynamic Network Embedding for Link Prediction

DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks

Joint Learning of Evolving Links for Dynamic Network Embedding

Continuous-Time Dynamic Network Embeddings

Co-Regularized Deep Multi-Network Embedding