- class brainpy.connect.ScaleFreeBADual(m1, m2, p, directed=False, seed=None)#
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.
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.
ConnectorError – If m1 and m2 do not satisfy
1 <= m1,m2 < nor p does not satisfy
0 <= p <= 1.
Moshiri “The dual-Barabasi-Albert model”, arXiv:1810.10538.
- __init__(m1, m2, p, directed=False, seed=None)#
__init__(m1, m2, p[, directed, seed])
build connections with certain data type.
make_returns(structures, conn_data[, csr, ...])
Make the desired synaptic structures and return them.