brainpy.connect.ScaleFreeBADual#

class brainpy.connect.ScaleFreeBADual(m1, m2, p, directed=False, seed=None)[source]#

Build a random graph according to the dual Barabási–Albert preferential attachment model.

A graph of :math::num_node nodes is grown by attaching new nodes each with either $m_1$ edges (with probability \(p\)) or \(m_2\) edges (with probability \(1-p\)) that are preferentially attached to existing nodes with high degree.

Parameters

  • m1 (int) – Number of edges to attach from a new node to existing nodes with probability \(p\)

  • m2 (int) – Number of edges to attach from a new node to existing nodes with probability \(1-p\)

  • p (float) – The probability of attaching \(m\_1\) edges (as opposed to \(m\_2\) edges)

  • seed (integer, random_state, or None (default)) – Indicator of random number generation state.

Raises

ConnectorError – If m1 and m2 do not satisfy 1 <= m1,m2 < n or p does not satisfy 0 <= p <= 1.

References

1

  1. Moshiri “The dual-Barabasi-Albert model”, arXiv:1810.10538.

__init__(m1, m2, p, directed=False, seed=None)[source]#

Methods

__init__(m1, m2, p[, directed, seed])

build_conn()

build connections with certain data type.

check(structures)

make_returns(structures, conn_data[, csr, ...])

Make the desired synaptic structures and return them.

require(*structures)

requires(*structures)