Netsmf: Large-scale network embedding as sparse matrix factorization
We study the problem of large-scale network embedding, which aims to learn latent
representations for network mining applications. Previous research shows that 1) popular …
representations for network mining applications. Previous research shows that 1) popular …
Differentially private recommender systems: Building privacy into the netflix prize contenders
F McSherry, I Mironov - Proceedings of the 15th ACM SIGKDD …, 2009 - dl.acm.org
We consider the problem of producing recommendations from collective user behavior while
simultaneously providing guarantees of privacy for these users. Specifically, we consider the …
simultaneously providing guarantees of privacy for these users. Specifically, we consider the …
Regularized spectral clustering under the degree-corrected stochastic blockmodel
T Qin, K Rohe - Advances in neural information processing …, 2013 - proceedings.neurips.cc
Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently,
Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate …
Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate …
Minimax rates in network analysis: Graphon estimation, community detection and hypothesis testing
C Gao, Z Ma - 2021 - projecteuclid.org
This paper surveys some recent developments in fundamental limits and optimal algorithms
for network analysis. We focus on minimax optimal rates in three fundamental problems of …
for network analysis. We focus on minimax optimal rates in three fundamental problems of …
Community detection in degree-corrected block models
Community detection in degree-corrected block models Page 1 The Annals of Statistics 2018,
Vol. 46, No. 5, 2153–2185 https://doi.org/10.1214/17-AOS1615 © Institute of Mathematical …
Vol. 46, No. 5, 2153–2185 https://doi.org/10.1214/17-AOS1615 © Institute of Mathematical …
Spectral clustering of graphs with general degrees in the extended planted partition model
K Chaudhuri, F Chung… - Conference on Learning …, 2012 - proceedings.mlr.press
In this paper, we examine a spectral clustering algorithm for similarity graphs drawn from a
simple random graph model, where nodes are allowed to have varying degrees, and we …
simple random graph model, where nodes are allowed to have varying degrees, and we …
Impact of regularization on spectral clustering
Impact of regularization on spectral clustering Page 1 The Annals of Statistics 2016, Vol. 44, No.
4, 1765–1791 DOI: 10.1214/16-AOS1447 © Institute of Mathematical Statistics, 2016 IMPACT …
4, 1765–1791 DOI: 10.1214/16-AOS1447 © Institute of Mathematical Statistics, 2016 IMPACT …
Convexified modularity maximization for degree-corrected stochastic block models
Convexified modularity maximization for degree-corrected stochastic block models Page 1
The Annals of Statistics 2018, Vol. 46, No. 4, 1573–1602 https://doi.org/10.1214/17-AOS1595 …
The Annals of Statistics 2018, Vol. 46, No. 4, 1573–1602 https://doi.org/10.1214/17-AOS1595 …
Graph partitioning via adaptive spectral techniques
A Coja-Oghlan - Combinatorics, Probability and Computing, 2010 - cambridge.org
In this paper we study the use of spectral techniques for graph partitioning. Let G=(V, E) be a
graph whose vertex set has a 'latent'partition V1,..., Vk. Moreover, consider a 'density …
graph whose vertex set has a 'latent'partition V1,..., Vk. Moreover, consider a 'density …
Snap, small-world network analysis and partitioning: An open-source parallel graph framework for the exploration of large-scale networks
We present SNAP (Small-world Network Analysis and Partitioning), an open-source graph
framework for exploratory study and partitioning of large-scale networks. To illustrate the …
framework for exploratory study and partitioning of large-scale networks. To illustrate the …