easygraph.functions.graph_generator.RandomNetwork module#

easygraph.functions.graph_generator.RandomNetwork.WS_Random(n, k, p, FilePath=None)[source]#

Returns a small-world graph.

Parameters:

  • n (int) – The number of nodes

  • k (int) – Each node is joined with its k nearest neighbors in a ring topology.

  • p (float) – The probability of rewiring each edge

  • FilePath (string) – The file for storing the output graph G

Returns:

G – a small-world graph

Return type:

graph

Examples

Returns a small-world graph G

>>> WS_Random(100,10,0.3,"/users/fudanmsn/downloads/RandomNetwork.txt")
easygraph.functions.graph_generator.RandomNetwork.erdos_renyi_M(n, edge, directed=False, FilePath=None)[source]#

Given the number of nodes and the number of edges, return an Erdős-Rényi random graph, and store the graph in a document.

Parameters:

  • n (int) – The number of nodes.

  • edge (int) – The number of edges.

  • directed (bool, optional (default=False)) – If True, this function returns a directed graph.

  • FilePath (string) – The file for storing the output graph G.

Returns:

G – an Erdős-Rényi random graph.

Return type:

graph

Examples

Returns an Erdős-Rényi random graph G.

>>> erdos_renyi_M(100,180,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")

References

[1]

  1. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).

[2]

    1. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).

easygraph.functions.graph_generator.RandomNetwork.erdos_renyi_P(n, p, directed=False, FilePath=None)[source]#

Given the number of nodes and the probability of edge creation, return an Erdős-Rényi random graph, and store the graph in a document.

Parameters:

  • n (int) – The number of nodes.

  • p (float) – Probability for edge creation.

  • directed (bool, optional (default=False)) – If True, this function returns a directed graph.

  • FilePath (string) – The file for storing the output graph G.

Returns:

G – an Erdős-Rényi random graph.

Return type:

graph

Examples

Returns an Erdős-Rényi random graph G

>>> erdos_renyi_P(100,0.5,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")

References

[1]

  1. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).

[2]

    1. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).

easygraph.functions.graph_generator.RandomNetwork.fast_erdos_renyi_P(n, p, directed=False, FilePath=None)[source]#

Given the number of nodes and the probability of edge creation, return an Erdős-Rényi random graph, and store the graph in a document. Use this function for generating a huge scale graph.

Parameters:

  • n (int) – The number of nodes.

  • p (float) – Probability for edge creation.

  • directed (bool, optional (default=False)) – If True, this function returns a directed graph.

  • FilePath (string) – The file for storing the output graph G.

Returns:

G – an Erdős-Rényi random graph.

Return type:

graph

Examples

Returns an Erdős-Rényi random graph G

>>> erdos_renyi_P(100,0.5,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")

References

[1]

  1. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).

[2]

    1. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).

easygraph.functions.graph_generator.RandomNetwork.graph_Gnm(num_v: int, num_e: int)[source]#

Return a random graph with num_v vertices and num_e edges. Edges are drawn uniformly from the set of possible edges.

Parameters:

  • num_v (int) – The Number of vertices.

  • num_e (int) – The Number of edges.

Examples

>>> import easygraph.randomhypergraph as rh
>>> g = rh.graph_Gnm(4, 5)
>>> g.e
([(1, 2), (0, 3), (2, 3), (0, 2), (1, 3)], [1.0, 1.0, 1.0, 1.0, 1.0])