Name
Abstract | ||
|---|---|---|
Exchangeable graph models (ExGM) subsume a number of popular network models. The mathematical object that characterizes an ExGM is termed a graphon. Finding scalable estimators of graphons, provably consistent, remains an open issue. In this paper, we propose a histogram estimator of a graphon that is provably consistent and numerically efficient. The proposed estimator is based on a sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree of a graph, then smooths the sorted graph using total variation minimization. The consistency of the SAS algorithm is proved by leveraging sparsity concepts from compressed sensing. |
Year | Venue | Field |
|---|---|---|
2014 | ICML | Histogram,Mathematical object,Computer science,Artificial intelligence,Compressed sensing,Graph,Mathematical optimization,Pattern recognition,Algorithm,Total variation minimization,Network model,Estimator,Scalability |
DocType | Citations | PageRank |
Conference | 10 | 0.70 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley H. Chan | 1 | 403 | 30.95 |
| Edoardo Airoldi | 2 | 709 | 59.54 |