Fetching the paper…
Reading the bibliography…
Graphs are a prevalent tool in data science, as they model the inherent structure of the data.
Collective dynamics of ‘small-world’networks
Duncan J Watts and Steven H Strogatz · 1998
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2001
Earlier work this paper cites.
Solving semidefinite-quadratic-linear programs using sdpt3
Reha H Tütüncü, Kim-Chuan Toh, and Michael J Todd · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, et al · 2003
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Linear prediction models with graph regularization for web-page categorization
Tong Zhang, Alexandrin Popescul, and Byron Dom · 2006
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
Efficient projections onto the l 1-ball for learning in high dimensions
John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra · 2008
Earlier work this paper cites.
Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing
Abderrahim Elmoataz, Olivier Lezoray, and Sébastien Bougleux · 2008
Earlier work this paper cites.
Label propagation through linear neighborhoods
Fei Wang and Changshui Zhang · 2008
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
Fitting a graph to vector data
Samuel I Daitch, Jonathan A Kelner, and Daniel A Spielman · 2009
Earlier work this paper cites.
Graph construction and b-matching for semi-supervised learning
Tony Jebara, Jun Wang, and Shih-Fu Chang · 2009
Earlier work this paper cites.
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja and David G Lowe · 2009
Cited alongside, same era.
Discovering structure by learning sparse graph
Brenden Lake and Joshua Tenenbaum · 2010
Cited alongside, same era.
Graph regularized nonnegative matrix factorization for data representation
Deng Cai, Xiaofei He, Jiawei Han, and Thomas S Huang · 2011
Cited alongside, same era.
Efficient k-nearest neighbor graph construction for generic similarity measures
Wei Dong, Charikar Moses, and Kai Li · 2011
Cited alongside, same era.
Graph regularized sparse coding for image representation
Miao Zheng, Jiajun Bu, Chun Chen, Can Wang, Lijun Zhang, Guang Qiu, and Deng Cai · 2011
Cited alongside, same era.
Fast matching of binary features
Marius Muja and David G Lowe · 2012
Cited alongside, same era.
Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems
Nikos Komodakis and Jean-Christophe Pesquet · 2015
Later among the works it cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
Later among the works it cites.
Robust principal component analysis on graphs
Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, and Pierre Vandergheynst · 2015
Later among the works it cites.
Song recommendation with non-negative matrix factorization and graph total variation
Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, and Pierre Vandergheynst · 2016
Later among the works it cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A graph theoretical regression model for brain connectivity learning of alzheimer’s disease
Chenhui Hu, Lin Cheng, Jorge Sepulcre, Georges El Fakhri, Yue M Lu, and Quanzheng Li · 2013
Cited alongside, same era.
Graph-laplacian pca: Closed-form solution and robustness
Bo Jiang, Chibiao Ding, Bio Luo, and Jin Tang · 2013
Cited alongside, same era.
Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, and Pierre Vandergheynst · 2014
Cited alongside, same era.
Scalable nearest neighbor algorithms for high dimensional data
Marius Muja and David G Lowe · 2014
Cited alongside, same era.
GSPBOX: A toolbox for signal processing on graphs
Nathanaël Perraudin, Johan Paratte, David Shuman, Vassilis Kalofolias, Pierre Vandergheynst, and David K. Hammond · 2014
Cited alongside, same era.
Learning laplacian matrix in smooth graph signal representations
Xiaowen Dong, Dorina Thanou, Pascal Frossard, and Pierre Vandergheynst · 2015
Cited alongside, same era.
How to learn a graph from smooth signals
Vassilis Kalofolias · 2016
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Later among the works it cites.
Yu A Malkov and DA Yashunin · 2016
Later among the works it cites.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M Bronstein · 2016
Later among the works it cites.
Fast robust pca on graphs
Nauman Shahid, Nathanael Perraudin, Vassilis Kalofolias, Gilles Puy, and Pierre Vandergheynst · 2016
Later among the works it cites.
Nathanaël Perraudin, Michaël Defferrard, Tomasz Kacprzak, and Raphael Sgier · 2018
Closest in time.
Spherical convergence maps dataset, July 2018
Raphael Sgier, Tomasz Kacprzak, Nathanaël Perraudin, and Michaël Defferrard · 2018
Closest in time.