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10.1038/s41598-018-29174-3 http://scihub22266oqcxt.onion/10.1038/s41598-018-29174-3 C6052167!6052167!30022035     free     free     free       Sci+Rep 2018 ; 8 (ä): ä Nephropedia Template TP {{tp|p=30022035|t=2018. Compressing Networks with Super Nodes |pdf=|usr=}}{{30022035}} gab.com Text Compressing Networks with Super Nodes JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=30022035 #FOAvip Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of ?super nodes?, where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the ?CoreHD? ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network. Twit Text FOAVip Compressing Networks with Super Nodes ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=30022035 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30022035 #FOAvip English Wikipedia <ref>{{Cite pmid|30022035}}</ref> Compressing Networks with Super Nodes #MMPMID30022035 Stanley N; Kwitt R; Niethammer M; Mucha PJ Sci Rep 2018[]; 8 (ä): ä PMID30022035show ga Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of ?super nodes?, where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the ?CoreHD? ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network. äCompressing Networks with Super Nodes (epmc).pdf DeepDyve Pubget Overpricing