- graphscope.nx.generators.community.gaussian_random_partition_graph(n, s, v, p_in, p_out, directed=False, seed=None)
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
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.
G – gaussian random partition graph
- Return type
NetworkX Graph or DiGraph
NetworkXError – If s is > n If p_in or p_out is not in [0,1]
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 
>>> G = nx.gaussian_random_partition_graph(100, 10, 10, 0.25, 0.1) >>> len(G) 100
Ulrik Brandes, Marco Gaertler, Dorothea Wagner, Experiments on Graph Clustering Algorithms, In the proceedings of the 11th Europ. Symp. Algorithms, 2003.