Must-read papers on NRL/NE.

NRL: network representation learning. NE: network embedding.

Contributed by Cunchao Tu and Yuan Yao.

We release OpenNE, an open source toolkit for NE/NRL. This repository provides a standard NE/NRL(Network Representation Learning）training and testing framework. Currently, the implemented models in OpenNE include DeepWalk, LINE, node2vec, GraRep, TADW and GCN.

Survey papers:

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

Journal and Conference papers:

1. DeepWalk: Online Learning of Social Representations. Bryan Perozzi, Rami Al-Rfou, Steven Skiena. KDD 2014. paper code

2. Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks. Yann Jacob, Ludovic Denoyer, Patrick Gallinar. WSDM 2014. paper

3. Non-transitive Hashing with Latent Similarity Componets. Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu. KDD 2015. 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 code

7. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. Jian Tang, Meng Qu, Qiaozhu Mei. KDD 2015. paper code

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 code

10. Asymmetric Transitivity Preserving Graph Embedding. Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu. KDD 2016. paper

11. Revisiting Semi-supervised Learning with Graph Embeddings. Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov. ICML 2016. paper

12. node2vec: Scalable Feature Learning for Networks. Aditya Grover, Jure Leskovec. KDD 2016. paper code

13. Max-Margin DeepWalk: Discriminative Learning of Network Representation. Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun. IJCAI 2016. paper code

14. Structural Deep Network Embedding. Daixin Wang, Peng Cui, Wenwu Zhu. KDD 2016. paper

15. Community Preserving Network Embedding. Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang. AAAI 2017. paper

16. Semi-supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017. paper code

17. CANE: Context-Aware Network Embedding for Relation Modeling. Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun. ACL 2017. paper code

18. Fast Network Embedding Enhancement via High Order Proximity Approximation. Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu. IJCAI 2017. paper code

19. TransNet: Translation-Based Network Representation Learning for Social Relation Extraction. Cunchao Tu, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun. IJCAI 2017. paper code

20. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami. KDD 2017. paper code

21. Learning from Labeled and Unlabeled Vertices in Networks. Wei Ye, Linfei Zhou, Dominik Mautz, Claudia Plant, Christian Böhm. KDD 2017. paper

22. Unsupervised Feature Selection in Signed Social Networks. Kewei Cheng, Jundong Li, Huan Liu. KDD 2017. paper

23. struc2vec: Learning Node Representations from Structural Identity. Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo. KDD 2017. paper code

24. Label Informed Attributed Network Embedding. Xiao Huang, Jundong Li, Xia Hu. WSDM 2017. paper code

25. Accelerated Attributed Network Embedding. Xiao Huang, Jundong Li, Xia Hu. SDM 2017. paper code

26. Inductive Representation Learning on Large Graphs. William L. Hamilton, Rex Ying, Jure Leskovec. NIPS 2017. paper code

27. Variation Autoencoder Based Network Representation Learning for Classification. Hang Li, Haozheng Wang, Zhenglu Yang, Masato Odagaki. ACL 2017. paper

28. Preserving Proximity and Global Ranking for Node Embedding. Yi-An Lai, Chin-Chi Hsu, Wenhao Chen, Mi-Yen Yeh, Shou-De Lin. NIPS 2017.

29. Learning Graph Embeddings with Embedding Propagation. Alberto Garcia Duran, Mathias Niepert. NIPS 2017. paper

30. Name Disambiguation in Anonymized Graphs using Network Embedding. Baichuan Zhang, Mohammad Al Hasan. CIKM 2017. paper

31. Enhancing the Network Embedding Quality with Structural Similarity. Tianshu Lyu, Yuan Zhang, Yan Zhang. CIKM 2017. paper

32. Attributed Signed Network Embedding. Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu. CIKM 2017. paper

33. Attributed Network Embedding for Learning in a Dynamic Environment. Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu. CIKM 2017. paper

34. HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. Tao-yang Fu, Wang-Chien Lee, Zhen Lei. CIKM 2017.

35. From Properties to Links: Deep Network Embedding on Incomplete Graphs. Dejian Yang, Senzhang Wang, Chaozhuo Li, Xiaoming Zhang, Zhoujun Li. CIKM 2017.

36. 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

37. On Embedding Uncertain Graphs. Jiafeng Hu, Reynold Cheng, Zhipeng Huang, Yixang Fang, Siqiang Luo. CIKM 2017. paper

38. 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

39. Learning Node Embeddings in Interaction Graphs. Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu. CIKM 2017. paper

40. Learning Community Embedding with Community Detection and Node Embedding on Graphs. Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin ChenChuan Chang, Erik Cambria. CIKM 2017. paper code

41. 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

42. Exploring Expert Cognition for Attributed Network Embedding. Xiao Huang, Qingquan Song, Jundong Li, Xia Ben Hu. WSDM 2018.

43. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu. WSDM 2018.

44. Multidimensional Network Embedding with Hierarchical Structures. Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin. WSDM 2018.

45. Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning. Meng Qu, Jian Tang, Jiawei Han. WSDM 2018.