Description Usage Arguments Value Author(s) References Examples

View source: R/HCD-Implement.R

Generates networks from binary tree stochastic block model, with provided sequence of connection probability along the tree

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`n` |
number of nodes in the network |

`d` |
number of layers until leaves (excluding the root) |

`a.seq` |
the connection probability sequence along the tree, a_r, see details in the paper |

`lambda` |
average node degree, only used when alpha is not provided |

`alpha` |
the common scaling of the a_r sequence. So at the end, essentially the a_r sequence is a.seq*alpha |

`N` |
the number of networks to generate from the same model |

A list of objections of

`A.list` |
the generated network adjacency matrices |

`B` |
the connection probability matrix between K communities, where K = 2^d |

`label` |
the vector of community labels for n nodes |

`P` |
the connection probability matrix between the n nodes. It is the expectation of adjacency matrices, except on the diagonal |

`comm.sim.mat` |
the binary string similarity matrix between communities |

`node.sim.mat` |
the binary string similarity matrix between nodes |

Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina.

Maintainer: Tianxi Li <[email protected]>

Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina. Hierarchical community detection by recursive partitioning. arXiv:1810.01509

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