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10.1371/journal.pone.0135177 http://scihub22266oqcxt.onion/10.1371/journal.pone.0135177 C4565681!4565681!26356296     free     free     free       PLoS+One 2015 ; 10 (9): ä Nephropedia Template TP {{tp|p=26356296|t=2015. Fast Generation of Sparse Random Kernel Graphs |pdf=|usr=}}{{26356296}} gab.com Text Fast Generation of Sparse Random Kernel Graphs JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26356296 #FOAvip The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ?(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. Twit Text FOAVip Fast Generation of Sparse Random Kernel Graphs ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26356296 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26356296 #FOAvip English Wikipedia <ref>{{Cite pmid|26356296}}</ref> Fast Generation of Sparse Random Kernel Graphs #MMPMID26356296 Hagberg A; Lemons N PLoS One 2015[]; 10 (9): ä PMID26356296show ga The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ?(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. äFast Generation of Sparse Random Kernel Graphs (epmc).pdf DeepDyve Pubget Overpricing