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The skip-gram model for learning word embeddings (Mikolov et al.
Generalized inversion of modified matrices
Carl D Meyer, Jr. 1973 · 1973
Earlier work this paper cites.
ARPACK users’ guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods . Vol. 6
Richard B Lehoucq, Danny C Sorensen, and Chao Yang. 1998 · 1998
Earlier work this paper cites.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik. 2000 · 2000
Earlier work this paper cites.
SciPy: Open source scientific tools for Python
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2001
Earlier work this paper cites.
Spectral partitioning of random graphs. In Proceedings 42nd IEEE Symposium on Foundations of Computer Science . IEEE, 529–537
Frank McSherry. 2001 · 2001
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems . 849–856
Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002 · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
Mikhail Belkin and Partha Niyogi. 2003 · 2003
Earlier work this paper cites.
Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In Proceedings of the thirty-sixth annual ACM symposium on Theory of computing . 81–90
Daniel A Spielman and Shang-Hua Teng. 2004 · 2004
Earlier work this paper cites.
A guide to NumPy . Vol. 1
Travis E Oliphant. 2006 · 2006
Earlier work this paper cites.
Graph embedding and extensions: A general framework for dimensionality reduction
Shuicheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Qiang Yang, and Stephen Lin. 2006 · 2006
Earlier work this paper cites.
LIBLINEAR: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008 · 2008
Earlier work this paper cites.
A social identity approach to identify familiar strangers in a social network. In Third International AAAI Conference on Weblogs and Social Media
Nitin Agarwal, Huan Liu, Sudheendra Murthy, Arunabha Sen, and Xufei Wang. 2009 · 2009
Cited alongside, same era.
The BioGRID interaction database: 2011 update
Chris Stark, Bobby-Joe Breitkreutz, Andrew Chatr-Aryamontri, Lorrie Boucher, Rose Oughtred, Michael S Livstone, Julie Nixon, Kimberly Van Auken, Xiaodong Wang, Xiaoqi Shi, et al · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
A simple, combinatorial algorithm for solving SDD systems in nearly-linear time. In Proceedings of the forty-fifth annual ACM symposium on Theory of computing . 911–920
Jonathan A Kelner, Lorenzo Orecchia, Aaron Sidford, and Zeyuan Allen Zhu. 2013 · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems . 3111–3119
Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web . International World Wide Web Conferences Steering Committee, 1067–1077
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Later among the works it cites.
A latent variable model approach to pmi-based word embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2016 · 2016
Later among the works it cites.
Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems . 3844–3852
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Later among the works it cites.
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Cited alongside, same era.
word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method
Yoav Goldberg and Omer Levy. 2014 · 2014
Cited alongside, same era.
Approaching optimality for solving SDD linear systems
Ioannis Koutis, Gary L Miller, and Richard Peng. 2014 · 2014
Cited alongside, same era.
Neural Word Embedding as Implicit Matrix Factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
Cited alongside, same era.
Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Cited alongside, same era.
Incremental computation of pseudo-inverse of Laplacian. In International Conference on Combinatorial Optimization and Applications . Springer, 729–749
Gyan Ranjan, Zhi-Li Zhang, and Daniel Boley. 2014 · 2014
Cited alongside, same era.
Word embedding revisited: A new representation learning and explicit matrix factorization perspective. In Twenty-Fourth International Joint Conference on Artificial Intelligence
Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong, and Enhong Chen. 2015 · 2015
Cited alongside, same era.
An eigenvalue representation for random walk hitting times and its application to the rook graph
Howard Levinson. [n.d.]
Cited in the paper.
Thomas N Kipf and Max Welling. 2016 · 2016
Later among the works it cites.
Skip-Gram- Zipf+ Uniform= Vector Additivity. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 69–76
Alex Gittens, Dimitris Achlioptas, and Michael W Mahoney. 2017 · 2017
Later among the works it cites.
Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 459–467
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Later among the works it cites.
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018 · 2018
Later among the works it cites.
What the vec? Towards probabilistically grounded embeddings. In Advances in Neural Information Processing Systems . 7465–7475
Carl Allen, Ivana Balazevic, and Timothy Hospedales. 2019 · 2019
Later among the works it cites.
Analogies Explained: Towards Understanding Word Embeddings. In International Conference on Machine Learning . 223–231
Carl Allen and Timothy Hospedales. 2019 · 2019
Later among the works it cites.
Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amaur Holanda de Souza, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. 2019 · 2019
Later among the works it cites.