Name
Abstract | ||
|---|---|---|
We consider a non-parametric perspective of analyzing network data. Our goal is to seek a limiting object of a sequence of exchangeable random arrays called the graphon. We propose a numerically efficient algorithm for estimating graphons and we show that the proposed algorithm yields a consistent estimate as the size of the graph grows. Preliminary experiments show that the algorithm is effective in estimating stochastic block-models and continuous graphons. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GlobalSIP.2013.6736873 | Global Conference Signal and Information Processing |
Keywords | Field | DocType |
graph theory,network theory (graphs),stochastic processes,exchangeable graph model estimation,exchangeable random arrays,graphon,stochastic blockmodel approximation,Network analysis,exchangeable random graph model,graphlet,graphon,non-parametric estimation,stochastic blockmodel | Graph theory,Graph,Mathematical optimization,Random graph,Algorithm,Stochastic process,Network data,Limiting,Mathematics | Conference |
ISSN | Citations | PageRank |
2376-4066 | 1 | 0.37 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley H. Chan | 1 | 403 | 30.95 |
| Thiago B. Costa | 2 | 1 | 0.37 |
| Edoardo Airoldi | 3 | 709 | 59.54 |