Search Machine Learning Repository: @inproceedings{icml2014c1_chan14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {A Consistent Histogram Estimator for Exchangeable Graph Models},
    Url = {http://jmlr.org/proceedings/papers/v32/chan14.pdf},
    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.},
    Author = {Stanley Chan and Edoardo Airoldi},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {208-216}
   }