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. build_coo() Build a coo sparse connection data. build_csr() Build a csr sparse connection data. build_mat() Build a binary matrix connection data. require(*structures) Require all the connection data needed. requires(*structures) Require all the connection data needed.

Attributes

 is_version2_style