Description Usage Arguments Details Value

The optimal community structure is a subdivision of the network into nonoverlapping groups of nodes in a way that maximizes the number of within-group edges, and minimizes the number of between-group edges. The modularity is a statistic that quantifies the degree to which the network may be subdivided into such clearly delineated groups.

1 | ```
UNC.modularity.louvain.und(W, gamma = 1, hierarchy = FALSE, seed = NA)
``` |

`W` |
: a Matrix - undirected weighted/binary connection matrix |

`gamma` |
: a float - resolution parameter. default value=1. Values 0 <= gamma < 1 detect larger modules while gamma > 1 detects smaller modules. |

`hierarchy` |
: a boolean - enables hier. output |

`seed` |
: an integer - random seed |

The Louvain algorithm is a fast and accurate community detection algorithm (as of writing). The algorithm may also be used to detect hierarchical community structure.

R Microbenchmark - Fast enough.. Unit: milliseconds expr min lq mean median uq max neval fun 8.890078 11.65477 12.90705 12.62741 13.85725 19.57911 100

WITH compile::cmpfun() - Fast! Unit: milliseconds expr min lq mean median uq max neval fun 6.015344 7.543102 9.385713 9.529057 10.69335 13.49019 100

Note: Function is not validated yet.

ciQ : a list - two elements where element one is 'ci', a vector (refined community affiliation network), and element two is 'Q', a float (optimized modularity metric).If hierarchical output enabled, becomes an Hx1 array of floats instead.

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