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Compressing Networks with Super Nodes.
Scientific Reports ( IF 4.6 ) Pub Date : 2018-Jul-18 , DOI: 10.1038/s41598-018-29174-3
Natalie Stanley 1 , Roland Kwitt 2 , Marc Niethammer 3 , Peter J Mucha 4
Affiliation  

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.

中文翻译:

压缩具有超级节点的网络。

社区检测是一种常用的技术,用于基于连接模式的相似性来识别网络中的组。为了便于在大型网络中进行社区检测,我们将网络重铸为一个较小的“超级节点”网络,其中每个超级节点都包含原始网络中的一个或多个节点。然后,我们可以使用此超级节点表示作为标准社区检测算法的输入。为了定义超级节点的种子或中心,我们应用了“ CoreHD”排名,这是一种应用于网络拆卸和回收问题的技术。我们通过分析两种常见的社区检测方法来测试我们的方法:使用Louvain算法的模块化最大化和用于拟合随机块模型的最大似然优化。
更新日期:2018-07-19
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