rGitcy / Network-Embedding-and-Application-papers

Some recommended papers on Network Embedding and Application

Contributed by Ruijie Wu and Chunyang Ruan

Survey papers:

1. Graph Embedding Techniques,Applications,and Performance: A Survey. Palash Goyal and Emilio Ferrara.Paper
2. Knowledge Graph Embedding: A Survey of Approaches and Applications. Quan Wang,Zhendong Mao,Bin Wang,and Li Guo.
3. Network Representation Learning: A Survey. Daokun Zhang, Jie Yin, Xingquan Zhu Senior Member, IEEE, Chengqi Zhang Senior Member, IEEE.
4. A Comprehensive Survey of Graph Embedding:Problems, Techniques and Applications. Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang.

Journal and Conference papers:

1. TransNet:Translation-Based Network Representation Learning for Social Relation Extraction.Cunchao Tu,Zhengyan Zhang,Zhiyuan Liu1, Maosong Sun.Paper Code
2. Max-Margin DeepWalk: Discriminative Learning of Network Representation. Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun.Paper Code
3. A Boosting Approach to Learning Graph Representations.Rajmonda S. Caceresy,Kevin M.Carter,and Jeremy Kun.
4. GANE: A Generative Adversarial Network Embedding. Huiting Hong, Xin Li and Mingzhong Wang.
5. MILE: A Multi-Level Framework for Scalable Graph Embedding. Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy.
6. A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information.Yunan Luo,Xinbin Zhao,Jingtian Zhou,Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, and Jianyang Zeng.Paper
7. A Tutorial on Network Embeddings. Haochen Chen, Bryan Perozzi, Rami Al-Rfou, and Steven Skiena.
8. A Unified Framework for Community Detection and Network Representation Learning.Cunchao Tu, Xiangkai Zeng, Hao Wang, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun, Bo Zhang,and Leyu Lin.
9. A New Model for Learning in Graph Domains. Marco Gori, Gabriele Monfardini, Franco Scarselli.
10. Active Discriminative Network Representation Learning. Li Gao, Hong Yang, Chuan Zhou,Jia Wu,Shirui Pan,Yue Hu.
11. Adaptive Sampling Towards Fast Graph Representation Learning. Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.
12. Adversarial Network Embedding. Quanyu Dai, Qiang Li, Jian Tang, Dan Wang.
13. Adversarially Regularized Graph Autoencoder. Shirui Pan, Ruiqi Hu, Guodong Long,Jing Jiang,Lina Yao,Chengqi Zhang.
14. An Attention-based Collaboration Framework for Multi-View Network Representation Learning. Meng Qu,Jian Tang, Jingbo Shang,Xiang Ren, Ming Zhang,Jiawei Han.Paper
15. ASPEM: Embedding Learning by Aspects in Heterogeneous Information Networks.Yu Shi,Huan Gui,Qi Zhu,Lance Kaplan,Jiawei Han.
16. Attributed Network Embedding with Micro-meso Structure.Juan-Hui Li, Chang-Dong Wang,Ling Huang,Dong Huang,Jian-Huang Lai,and Pei Chen.Paper
17. Biomedical Text Categorization with Concept Graph Representations Using a Controlled Vocabulary. Meenakshi Mishra, Jun Huan, Said Bleik, Min Song.
18. PNE: Label Embedding Enhanced Network Embedding.Weizheng Chen, Xianling Mao, Xiangyu Li,Yan Zhang,and Xiaoming Li.
19. BASSI: Balance and Status Combined Signed Network Embedding. Yiqi Chen,Tieyun Qian,Ming Zhong,and Xuhui Li.Paper
20. Constructing Narrative Event Evolutionary Graph for Script Event Prediction. Zhongyang Li, Xiao Ding, Ting Liu.Paper Data and Code
21. Database Systems for Advanced Applications.Paper
22. Deep Inductive Network Representation Learning. Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed.Paper
23. Deep Neural Networks for Learning Graph Representations. Shaosheng Cao, Wei Lu, Qiongkai Xu.
24. DeepWalk: Online Learning of Social Representations. Bryan Perozzi, Rami Al-Rfou, Steven Skiena.Paper
25. Distributed Representations of Words and Phrases and their Compositionality.Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean.
26. Scientific Article Recommendation by using Distributed Representations of Text and Graph.Shashank Gupta, Vasudeva Varma.Paper
27. Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks.Yu Shi,Qi Zhu,Fang Guo Chao,Zhang Jiawei Han.Paper
28. From Properties to Links: Deep Network Embedding on Incomplete Graphs.Dejian Yang, Senzhang Wang, Chaozhuo Li, Xiaoming Zhang, Zhoujun Li.Paper
29. GATED GRAPH SEQUENCE NEURAL NETWORKS. Yujia Li,Richard Zemel, Marc Brockschmidt,Daniel Tarlow.
30. Graph Attention Networks. Petar Veliˇckovi´, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li`o, Yoshua Bengio.
31. Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks.Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, MichaelWitbrock, Vadim Sheinin.
32. Graph2vec: Learning Distributed Representations of Graphs. Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu and Shantanu Jaiswal.
33. GraphGAN: Graph Representation Learning with Generative Adversarial Nets.Hongwei Wang,JiaWang,JialinWang,Miao Zhao,Weinan Zhang, Fuzheng Zhang,Xing Xie and Minyi Guo.
34. Graph-Structured Representations for Visual Question Answering. Damien Teney,Lingqiao Liu,Anton van den Hengel.
35. Learning Structural Node Embeddings via Diffusion Wavelets.Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.Paper
36. GraRep: Learning Graph Representations with Global Structural Information. Shaosheng Cao, Wei Lu, Qiongkai Xu.Paper
37. Learning Deep Representations for Graph Clustering. Fei Tian, Bin Gao, Qing Cui, Enhong Chen, Tie-Yan Liu.
38. Representation Learning on Graphs: Methods and Applications. William L. Hamilton, Rex Ying, Jure Leskovec.
39. Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective. Junliang Guo, Linli Xu, Xunpeng Huang, and Enhong Chen.Paper
40. Heterogeneous Network Embedding via Deep Architectures. Shiyu Chang, Wei Han,Jiliang Tang,Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang.Paper
41. Heterogeneous Information Network Embedding for Meta Path based Proximity. Zhipeng Huang, Nikos Mamoulis.Paper
42. HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. Tao-yang Fu, Wang-Chien Lee, Zhen Lei.Paper
43. HINE: Heterogeneous Information Network Embedding. Yuxin Chen and Chenguang Wang.
44. Heterogeneous Information Network Embedding for Recommendation. Chuan Shi,Member,IEEE,Binbin Hu,Wayne Xin Zhao Member,IEEE and Philip S.Yu, Fellow, IEEE.
45. Knowledge Graph Embedding with Triple Context. Jun Shi, Huan Gao, Guilin Qi, Zhangquan Zhou.Paper
46. Learning Convolutional Neural Networks for Graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.
47. Learning Community Embedding with Community Detection and Node Embedding on Graphs. Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria.Paper
48. Learning Word Representations from Relational Graphs. Danushka Bollegala,Takanori Maehara ,Yuichi Yoshida, Ken-ichi Kawarabayashi.
49. Attributed Network Embedding with Micro-meso Structure. Juan-Hui Li, Chang-Dong Wang, Ling Huang, Dong Huang,Jian-Huang Lai, and Pei Chen.Paper
50. LINE: Large-scale Information Network Embedding. Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei.Paper
51. Matching Node Embeddings for Graph Similarity. Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis.Paper Code
52. Max-Margin DeepWalk: Discriminative Learning of Network Representation. Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun.Paper Data
53. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami.Paper
54. MolGAN: An implicit generative model for small molecular graphs. Nicola De Cao, Thomas Kipf.
55. Multi-Document Summarization Based on Two-Level Sparse Representation Model. He Liu, Hongliang Yu, Zhi-Hong Deng.
56. 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.Paper
57. NetGAN: Generating Graphs via RandomWalks. Aleksandar Bojchevski,Oleksandr Shchur,Daniel Zugner ,Stephan Gunnemann.
58. node2vec: Scalable Feature Learning for Networks. Aditya Grover, Jure Leskovec.Paper
59. CANE: Context-Aware Network Embedding for Relation Modeling. Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun.Paper
60. Preserving Local and Global Information for Network Embedding. Yao Ma, Suhang Wang, Zhaochun Ren, Dawei Yin, Jiliang Tang.
61. PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction. Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li.Paper
62. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. Jian Tang, Meng Qu, Qiaozhu Mei.Paper
63. Reducing the Dimensionality of Data with Neural Networks. G.E. Hinton and R.R. Salakhutdinov.Paper
64. Relational inductive biases, deep learning, and graph networks. Peter W. Battaglia1,Jessica B. Hamrick1,Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski1, 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.
65. Relational recurrent neural networks. Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap.Paper Code
66. Representation Learning for Scale-Free Networks. Rui Feng, Yang Yang, Wenjie Hu, FeiWu, Yueting Zhuang.
67. Representation Learning on Graphs: Methods and Applications. William L. Hamilton, Rex Ying, Jure Leskovec.
68. Residual Recurrent Neural Networks for Learning Sequential Representations. Boxuan Yue, Junwei Fu and Jun Liang.Paper
69. Revisiting Semi-Supervised Learning with Graph Embeddings. Zhilin Yang, William W. Cohen. Ruslan Salakhutdinov.Paper Code
70. RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding. ZhengWang, Xiaojun Ye, ChaokunWang, YuexinWu, ChangpingWang, Kaiwen Liang.
71. Structural Deep Embedding for Hyper-Networks. Ke Tu, Peng Cui, XiaoWang, FeiWang, Wenwu Zhu.
72. Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding. Zemin Liu, VincentW. Zheng, Zhou Zhao, Fanwei Zhu,Kevin Chen-Chuan Chang, MinghuiWu, Jing Ying.Paper code
73. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS. Thomas N. Kipf, Max Welling.Paper Code
74. Semi-Supervised Network Embedding. Chaozhuo Li, Zhoujun Li, Senzhang Wang, Yang Yang, Xiaoming Zhang, and Jianshe Zhou.
75. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu.Paper
76. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS. Thomas N. Kipf, Max Welling.Paper Code
77. Structural Deep Network Embedding. Daixin Wang, Peng Cui, Wenwu Zhu.Paper
78. The Graph Neural Network Model. Franco Scarselli, Marco Gori, Fellow, IEEE, Ah Chung Tsoi, Markus Hagenbuchner, Member, IEEE, and Gabriele Monfardini.Paper
79. Unsupervised Large Graph Embedding. Feiping Nie, Wei Zhu, Xuelong Li.
80. Vector-based similarity measurements for historical figures. YanqingChen,BryanPerozzi,StevenSkiena.Paper
81. SNE: Signed Network Embedding. Shuhan Yuan, Xintao Wu, and Yang Xiang.
82. Learning Product Embedding from Multi-relational User Behavior. Zhao Zhang, Weizheng Chen, Xiaoxuan Ren, and Yan Zhang.Paper
83. MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding. Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang.Paper
84. DeepCas: an End-to-end Predictor of Information Cascades.Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei.Paper
85. Learning and Transferring IDs Representation in E-commerce.Kui Zhao, Yuechuan Li, Zhaoqian Shuai, Cheng Yang.Paper
86. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.Jizhe Wang, Pipei Huan, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee.Paper
87. Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs. Zemin Liu, Vincent W. Zheng, Zhou Zhao, Zhao Li, Hongxia Yang, Minghui Wu, Jing Ying.Paper
88. Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.Xiaoxia Liu, Zhihao Yang, Shengtian Sang, Ziwei Zhou, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang and Bo Xu Paper
89. A Semi-Supervised Network Embedding Model for Protein Complexes Detection.Wei Zhao, Jia Zhu, Min Yang, Danyang Xiao, Gabriel Pui Cheong Fung, Xiaojun Chen
90. ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data.Sendong Zhao, Meng Jiang, Quan Yuan, Bing Qin, Ting Liu, ChengXiang Zhai
91. Role action embeddings: scalable representation of network positions.George Berry
92. SKIP-GRAPH: LEARNING GRAPH EMBEDDINGS WITH AN ENCODER-DECODER MODEL.John Boaz Lee & Xiangnan Kong
93. Learning Graph Representations with Recurrent Neural Network Autoencoders.Aynaz Taheri, Kevin Gimpel, Tanya Berger-Wolf
94. Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks.LUDOVIC DOS SANTOS, BENJAMIN PIWOWARSKI, LUDOVIC DENOYER, and PATRICK GALLINARI Paper
95. Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks.Yann Jacob, Ludovic Denoyer,Patrick Gallinari Paper
96. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.Jiaxi Tang, Ke Wang Paper
97. Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning.Meng Qu, Jian Tang, Jiawei Han Paper
98. Multi-Dimensional Network Embedding with Hierarchical Structure.Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin Paper
99. Exploring Expert Cognition for Attributed Network Embedding.Xiao Huang, Qingquan Song, Jundong Li, Xia Hu Paper
100. GRAM: Graph-based Attention Model for Healthcare Representation Learning. Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun
101. Deep Recursive Network Embedding with Regular Equivalence.Paper
102. Hierarchical Taxonomy Aware Network Embedding.Paper
103. Learning Deep Network Representations with Adversarially Regularized Autoencoders.Paper
104. NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks.Paper
105. On Interpretation of Network Embedding via Taxonomy InductionPaper
106. Deep Graph Infomax
107. Learning Graph Embedding with Adversarial Training Methods.Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, and Chengqi Zhang, Senior Member, IEEE