Fetching the paper…
Reading the bibliography…
Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis.
Direct and indirect methods for structural equivalence
Vladimir Batagelj, Anuška Ferligoj, and Patrick Doreian · 1992
Earlier work this paper cites.
Topic-sensitive pagerank
Taher H Haveliwala · 2002
Earlier work this paper cites.
Biogrid: a general repository for interaction datasets
Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, and Mike Tyers · 2006
Earlier work this paper cites.
The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2007
Earlier work this paper cites.
In-degree and pagerank: why do they follow similar power laws?
Nelly Litvak, Werner RW Scheinhardt, and Yana Volkovich · 2007
Earlier work this paper cites.
Toward accurate dynamic time warping in linear time and space
Stan Salvador and Philip Chan · 2007
Earlier work this paper cites.
Google’s PageRank and beyond: The science of search engine rankings
Amy N Langville and Carl D Meyer · 2011
Earlier work this paper cites.
Reprint of: The anatomy of a large-scale hypertextual web search engine
Sergey Brin and Lawrence Page · 2012
Earlier work this paper cites.
Mining structural hole spanners through information diffusion in social networks
Tiancheng Lou and Jie Tang · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Cited alongside, same era.
SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl · 2014
Cited alongside, same era.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 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.
Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Cited alongside, same era.
Network representation learning with rich text information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang · 2015
Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Later among the works it cites.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
Later among the works it cites.
A comprehensive survey of graph embedding: Problems, techniques and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2017
Later among the works it cites.
Enhancing network embedding with auxiliary information: An explicit matrix factorization perspective
Junliang Guo, Linli Xu, Xunpeng Huang, and Enhong Chen · 2017
Later among the works it cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Rain: Social role-aware information diffusion
Yang Yang, Jie Tang, Cane Wing-ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
Later among the works it cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
Later among the works it cites.
Deep attributed network embedding
Hongchang Gao and Heng Huang · 2018
Closest in time.
Deep recursive network embedding with regular equivalence
Ke Tu, Peng Cui, Xiao Wang, Philip S Yu, and Wenwu Zhu · 2018
Closest in time.
Arbitrary-order proximity preserved network embedding
Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao, and Wenwu Zhu · 2018
Closest in time.