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Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years.
Incorporate group information to enhance network embedding
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The meaning and use of the area under a receiver operating characteristic (roc) curve
James A Hanley and Barbara J McNeil. 1982 · 1982
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Making large-scale svm learning practical
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford. 2000 · 2000
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Relational learning via latent social dimensions
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Mixed membership stochastic blockmodels
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Adam: A method for stochastic optimization
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Deepwalk: Online learning of social representations
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015 · 2015
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
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Network representation learning with rich text information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. 2015 · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec. 2016 · 2016
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Pairwise word interaction modeling with deep neural networks for semantic similarity measurement
Hua He and Jimmy Lin. 2016 · 2016
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Max-margin deepwalk: Discriminative learning of network representation
Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, and Maosong Sun. 2016 · 2016
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Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu. 2016 · 2016
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap. 2017 · 2017
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Deconvolutional latent-variable model for text sequence matching
Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, and Lawrence Carin. 2017 · 2017
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Cane: Context-aware network embedding for relation modeling
Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. 2017 · 2017
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A compare-aggregate model for matching text sequences
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016 · 2016
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A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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A general framework for content-enhanced network representation learning
Xiaofei Sun, Jiang Guo, Xiao Ding, and Ting Liu. 2016 · 2016
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Learning context-aware convolutional filters for text processing
Dinghan Shen, Renqiang Min Martin, Yitong Li, and Lawrence Carin. 2018a
Cited in the paper.
Nash: Toward end-to-end neural architecture for generative semantic hashing
Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Lawrence Carin, and Ricardo Henao. 2018b
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Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, and Lawrence Carin. 2018c
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Shuohang Wang and Jing Jiang. 2017 · 2017
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Deconvolutional paragraph representation learning
Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, and Lawrence Carin. 2017 · 2017
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Multi-view sentence representation learning
Shuai Tang and Virginia R de Sa. 2018 · 2018
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Diffusion maps for textual network embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen, and Lawrence Carin. 2018 · 2018
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