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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.doi:10.1109/globalsip.2013.6736873 dblp:conf/globalsip/ChanCA13 fatcat:e3yc6jj6tvg25h6h5hno75uv64