Graphon Estimation
5 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Graphon Estimation
Netzschleuder
Latest papers with no code
Graph data augmentation with Gromow-Wasserstein Barycenters
This is primarily due to the complex and non-Euclidean nature of graph data.
Private graphon estimation via sum-of-squares
We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant number of blocks.
Computational Lower Bounds for Graphon Estimation via Low-degree Polynomials
From the statistical perspective, the minimax error rate of graphon estimation has been established by Gao et al (2015) for both stochastic block model (SBM) and nonparametric graphon estimation.
Graphon Estimation in bipartite graphs with observable edge labels and unobservable node labels
This is presented as a problem of estimation of a bivariate function referred to as graphon.
Training Graph Neural Networks by Graphon Estimation
In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data.
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing
This paper surveys some recent developments in fundamental limits and optimal algorithms for network analysis.
Consistent polynomial-time unseeded graph matching for Lipschitz graphons
We propose a consistent polynomial-time method for the unseeded node matching problem for networks with smooth underlying structures.
Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations
Many applications, including rank aggregation, crowd-labeling, and graphon estimation, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and/or columns.
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models.
Link prediction for egocentrically sampled networks
Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes.
