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Community detection (igraph algorithms (Multilevel (Considered the best by…
Community detection
igraph algorithms
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Multilevel
Considered the best by Yang et al., 2016. A comparative analysis of community detection algorithms on artificial networks. Nature Scientific Reports 6:30750, DOI: 10.1038/srep30750
Originally proposed by Blondel et al., 2008. Fast unfolding of communities in large networks. Journal of Statistical
Mechanics: Theory and Experiment P10008.
Infomap, Label propagation and Walktrap also good when taking time for detection in to account
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First good to apply either a Spinglass algorithm or Multilevel algorithm in order to obtain an estimate of the value of the mixing parameter μ.
Community definition: Identifies groups of nodes that are heavily connected among themselves, but sparsely connected to the rest of the network
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Obtaining real networks to which a ground truth can be associated is difficult and costly in terms of both economics and time.
The mixing parameter (μ) represents the fraction of edges a node has with nodes belonging to other communities with respect to its total degree