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Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.
An information flow model for conflict and fission in small groups
Wayne W. Zachary · 1977
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Multidimensional Scaling, Second Edition
Trevor F. Cox and M.A.A. Cox · 2000
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2001
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Libsvm: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Heat kernel based community detection
Kyle Kloster and David F. Gleich · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Learning deep representations for graph clustering
Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu · 2014
Cited alongside, same era.
Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
Cited alongside, same era.
Heterogeneous network embedding via deep architectures
Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, and Thomas S. Huang · 2015
Cited alongside, same era.
Community detection via measure space embedding
Mark Kozdoba and Shie Mannor · 2015
Cited alongside, same era.
Context-dependent knowledge graph embedding
Yuanfei Luo, Quan Wang, Bin Wang, and Li Guo · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
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Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
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Representation learning of knowledge graphs with entity descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun · 2016
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Modularity based community detection with deep learning
Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, and Weixiong Zhang · 2016
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Cited alongside, same era.
Community-based question answering via heterogeneous social network learning
Hanyin Fang, Fei Wu, Zhou Zhao, Xinyu Duan, Yueting Zhuang, and Martin Ester · 2016
Cited alongside, same era.
Learning community embedding with community detection and node embedding on graphs
Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria · 2017
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Semantic proximity search on heterogeneous graph by proximity embedding
Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chen-Chuan Chang, Minghui Wu, and Jing Ying · 2017
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