graphscope.nx.generators.community.gaussian_random_partition_graph#

graphscope.nx.generators.community.gaussian_random_partition_graph(n, s, v, p_in, p_out, directed=False, seed=None)[source]#

Generate a Gaussian random partition graph.

A Gaussian random partition graph is created by creating k partitions each with a size drawn from a normal distribution with mean s and variance s/v. Nodes are connected within clusters with probability p_in and between clusters with probability p_out[1]

Parameters
• n (int) – Number of nodes in the graph

• s (float) – Mean cluster size

• v (float) – Shape parameter. The variance of cluster size distribution is s/v.

• p_in (float) – Probabilty of intra cluster connection.

• p_out (float) – Probability of inter cluster connection.

• directed (boolean, optional default=False) – Whether to create a directed graph or not

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

Returns

G – gaussian random partition graph

Return type

NetworkX Graph or DiGraph

Raises

NetworkXError – If s is > n If p_in or p_out is not in [0,1]

Notes

Note the number of partitions is dependent on s,v and n, and that the last partition may be considerably smaller, as it is sized to simply fill out the nodes [1]

Examples

>>> G = nx.gaussian_random_partition_graph(100, 10, 10, 0.25, 0.1)
>>> len(G)
100


References

1

Ulrik Brandes, Marco Gaertler, Dorothea Wagner, Experiments on Graph Clustering Algorithms, In the proceedings of the 11th Europ. Symp. Algorithms, 2003.