Fetching the paper…
Reading the bibliography…
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions.
Multidimensional scaling: I. Theory and method
Warren S Torgerson · 1952
Earlier work this paper cites.
Principles of mathematical analysis , volume 3
Walter Rudin · 1976
Earlier work this paper cites.
Stochastic blockmodels: First steps
Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
Earlier work this paper cites.
On the representation theorem for exchangeable arrays
Olav Kallenberg · 1989
Earlier work this paper cites.
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller · 1998
Earlier work this paper cites.
Improving support vector machine classifiers by modifying kernel functions
Shun-ichi Amari and Si Wu · 1999
Earlier work this paper cites.
Probabilistic principal component analysis
Michael E Tipping and Christopher M Bishop · 1999
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
Earlier work this paper cites.
A course in metric geometry , volume 33
Dmitri Burago · 2001
Earlier work this paper cites.
Latent space approaches to social network analysis
Peter D Hoff, Adrian E Raftery, and Mark S Handcock · 2002
Earlier work this paper cites.
Gaussian process latent variable models for visualisation of high dimensional data
Neil D Lawrence · 2003
Earlier work this paper cites.
Probabilistic non-linear principal component analysis with Gaussian process latent variable models
Neil Lawrence and Aapo Hyvärinen · 2005
Earlier work this paper cites.
Riemannian geometry , volume 171 of Graduate Texts in Mathematics
Peter Petersen · 2006
Earlier work this paper cites.
Automatic dimensionality selection from the scree plot via the use of profile likelihood
Mu Zhu and Ali Ghodsi · 2006
Earlier work this paper cites.
The phase transition in inhomogeneous random graphs
Béla Bollobás, Svante Janson, and Oliver Riordan · 2007
Earlier work this paper cites.
Mixed membership stochastic blockmodels
Edoardo M Airoldi, David M Blei, Stephen E Fienberg, and Eric P Xing · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Support vector machines
Ingo Steinwart and Andreas Christmann · 2008
Earlier work this paper cites.
Introduction to metric and topological spaces
Wilson A Sutherland · 2009
Earlier work this paper cites.
Graph-embedding for speaker recognition
Zahi N. Karam and William M. Campbell · 2010
Earlier work this paper cites.
Bayesian Gaussian process latent variable model
Michalis Titsias and Neil D Lawrence · 2010
Earlier work this paper cites.
Stochastic blockmodels and community structure in networks
Brian Karrer and Mark EJ Newman · 2011
Earlier work this paper cites.
Spectral clustering and the high-dimensional stochastic blockmodel
Karl Rohe, Sourav Chatterjee, and Bin Yu · 2011
Cited alongside, same era.
Random feature maps for dot product kernels
Purushottam Kar and Harish Karnick · 2012
Cited alongside, same era.
Large networks and graph limits. American Mathematical Society Colloquium Publications , volume 60
László Lovász · 2012
Cited alongside, same era.
A consistent adjacency spectral embedding for stochastic blockmodel graphs
Daniel L Sussman, Minh Tang, Donniell E Fishkind, and Carey E Priebe · 2012
Cited alongside, same era.
Pseudo-likelihood methods for community detection in large sparse networks
Arash A Amini, Aiyou Chen, Peter J Bickel, and Elizaveta Levina · 2013
Cited alongside, same era.
Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
Joshua Cape, Minh Tang, and Carey E Priebe · 2019
Later among the works it cites.
Spectral embedding of weighted graphs
Ian Gallagher, Andrew Jones, Anna Bertiger, Carey Priebe, and Patrick Rubin-Delanchy · 2019
Later among the works it cites.
On a two-truths phenomenon in spectral graph clustering
Carey E Priebe, Youngser Park, Joshua T Vogelstein, John M Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, and Eric Bridgeford · 2019
Later among the works it cites.
Comparison of brain connectomes using geodesic distance on manifold: A twins study
Abubakar Yamin, Michael Dayan, Letizia Squarcina, Paolo Brambilla, Vittorio Murino, V Diwadkar, and Diego Sona · 2019
Later among the works it cites.
The multilayer random dot product graph
Andrew Jones and Patrick Rubin-Delanchy · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Peter Bickel, David Choi, Xiangyu Chang, and Hai Zhang · 2013
Cited alongside, same era.
Variational bayesian inference for the latent position cluster model for network data
Michael Salter-Townshend and Thomas Brendan Murphy · 2013
Cited alongside, same era.
Universally consistent vertex classification for latent positions graphs
Minh Tang, Daniel L Sussman, and Carey E Priebe · 2013
Cited alongside, same era.
Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
Vince Lyzinski, Daniel L. Sussman, Minh Tang, Avanti Athreya, and Carey E. Priebe · 2014
Cited alongside, same era.
Rate-optimal graphon estimation
Chao Gao, Yu Lu, and Harrison H Zhou · 2015
Cited alongside, same era.
Consistency of spectral clustering in stochastic block models
Jing Lei and Alessandro Rinaldo · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Later among the works it cites.
Population genomics of the viking world
Ashot Margaryan, Daniel J Lawson, Martin Sikora, Fernando Racimo, Simon Rasmussen, Ida Moltke, Lara M Cassidy, Emil Jørsboe, Andrés Ingason, Mikkel W Pedersen, et al · 2020
Later among the works it cites.
Manifold structure in graph embeddings
Patrick Rubin-Delanchy · 2020
Later among the works it cites.
A statistical interpretation of spectral embedding: the generalised random dot product graph
Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, and Carey E Priebe · 2020
Later among the works it cites.
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel
Francesco Sanna Passino, Nicholas A Heard, and Patrick Rubin-Delanchy · 2020
Later among the works it cites.
Learning 1-dimensional submanifolds for subsequent inference on random dot product graphs
Michael W Trosset, Mingyue Gao, Minh Tang, and Carey E Priebe · 2020
Later among the works it cites.
http://berkeleyearth.org
Berkeley Earth · 2021
Closest in time.
https://www.npr.org/sections/goatsandsoda/2020/05/15/855669867/countries-slammed-their-borders-shut-to-stop-coronavirus-but-is-it-doing-any-goo
Countries slammed their borders shut to stop coronavirus. but is it doing any good? · 2021
Closest in time.
https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
Climate change: Earth surface temperature data · 2021
Closest in time.
https://simplemaps.com/resources/free-country-cities
Simplemaps free entire world database · 2021
Closest in time.
On the estimation of latent distances using graph distances
Ery Arias-Castro, Antoine Channarond, Bruno Pelletier, and Nicolas Verzelen · 2021
Closest in time.
On estimation and inference in latent structure random graphs
Avanti Athreya, Minh Tang, Youngser Park, and Carey E Priebe · 2021
Closest in time.
Can smooth graphons in several dimensions be represented by smooth graphons on [ 0 , 1 ] [0,1] ?
Svante Janson and Sofia Olhede · 2021
Closest in time.
Network representation using graph root distributions
Jing Lei · 2021
Closest in time.
Spectral clustering under degree heterogeneity: a case for the random walk Laplacian
Alexander Modell and Patrick Rubin-Delanchy · 2021
Closest in time.
Crowdsourced air traffic data from the opensky network 2019–2020
Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders · 2021
Closest in time.
Consistency of random-walk based network embedding algorithms
Yichi Zhang and Minh Tang · 2021
Closest in time.