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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]

  • 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 (int) – Seed value for random number generator

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 [1]


>>> G = nx.gaussian_random_partition_graph(100,10,10,.25,.1)
>>> len(G)


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