chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import math
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import random
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import easygraph as eg
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from easygraph.classes.graph import Graph
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__all__ = [
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"erdos_renyi_M",
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"erdos_renyi_P",
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"fast_erdos_renyi_P",
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"WS_Random",
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"graph_Gnm",
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]
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def erdos_renyi_M(n, edge, directed=False, FilePath=None):
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"""Given the number of nodes and the number of edges, return an Erdős-Rényi random graph, and store the graph in a document.
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Parameters
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----------
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n : int
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The number of nodes.
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edge : int
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The number of edges.
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directed : bool, optional (default=False)
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If True, this function returns a directed graph.
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FilePath : string
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The file for storing the output graph G.
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Returns
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-------
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G : graph
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an Erdős-Rényi random graph.
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Examples
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--------
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Returns an Erdős-Rényi random graph G.
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>>> erdos_renyi_M(100,180,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")
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References
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----------
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.. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
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.. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
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"""
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if directed:
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G = eg.DiGraph()
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adjacent = {}
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mmax = n * (n - 1)
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if edge >= mmax:
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for i in range(n):
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for j in range(n):
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if i != j:
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G.add_edge(i, j)
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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return G
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count = 0
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while count < edge:
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i = random.randint(0, n - 1)
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j = random.randint(0, n - 1)
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if i == j or G.has_edge(i, j):
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continue
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else:
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count = count + 1
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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G.add_edge(i, j)
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else:
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G = eg.Graph()
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adjacent = {}
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mmax = n * (n - 1) / 2
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if edge >= mmax:
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for i in range(n):
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for j in range(n):
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if i != j:
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G.add_edge(i, j)
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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if j not in adjacent:
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adjacent[j] = []
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adjacent[j].append(i)
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else:
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adjacent[j].append(i)
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return G
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count = 0
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while count < edge:
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i = random.randint(0, n - 1)
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j = random.randint(0, n - 1)
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if i == j or G.has_edge(i, j):
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continue
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else:
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count = count + 1
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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if j not in adjacent:
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adjacent[j] = []
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adjacent[j].append(i)
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else:
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adjacent[j].append(i)
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G.add_edge(i, j)
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writeRandomNetworkToFile(n, adjacent, FilePath)
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return G
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def erdos_renyi_P(n, p, directed=False, FilePath=None):
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"""Given the number of nodes and the probability of edge creation, return an Erdős-Rényi random graph, and store the graph in a document.
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Parameters
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----------
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n : int
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The number of nodes.
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p : float
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Probability for edge creation.
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directed : bool, optional (default=False)
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If True, this function returns a directed graph.
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FilePath : string
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The file for storing the output graph G.
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Returns
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-------
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G : graph
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an Erdős-Rényi random graph.
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Examples
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--------
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Returns an Erdős-Rényi random graph G
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>>> erdos_renyi_P(100,0.5,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")
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References
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----------
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.. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
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.. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
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"""
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if directed:
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G = eg.DiGraph()
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adjacent = {}
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probability = 0.0
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for i in range(n):
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for j in range(i + 1, n):
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probability = random.random()
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if probability < p:
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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G.add_edge(i, j)
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else:
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G = eg.Graph()
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adjacent = {}
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probability = 0.0
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for i in range(n):
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for j in range(i + 1, n):
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probability = random.random()
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if probability < p:
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(j)
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else:
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adjacent[i].append(j)
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if j not in adjacent:
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adjacent[j] = []
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adjacent[j].append(i)
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else:
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adjacent[j].append(i)
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G.add_edge(i, j)
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writeRandomNetworkToFile(n, adjacent, FilePath)
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return G
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def fast_erdos_renyi_P(n, p, directed=False, FilePath=None):
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"""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.
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Parameters
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----------
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n : int
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The number of nodes.
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p : float
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Probability for edge creation.
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directed : bool, optional (default=False)
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If True, this function returns a directed graph.
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FilePath : string
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The file for storing the output graph G.
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Returns
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-------
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G : graph
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an Erdős-Rényi random graph.
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Examples
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--------
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Returns an Erdős-Rényi random graph G
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>>> erdos_renyi_P(100,0.5,directed=False,FilePath="/users/fudanmsn/downloads/RandomNetwork.txt")
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References
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----------
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.. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
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.. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
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"""
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if directed:
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G = eg.DiGraph()
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w = -1
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lp = math.log(1.0 - p)
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v = 0
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adjacent = {}
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while v < n:
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lr = math.log(1.0 - random.random())
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w = w + 1 + int(lr / lp)
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if v == w: # avoid self loops
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w = w + 1
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while v < n <= w:
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w = w - n
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v = v + 1
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if v == w: # avoid self loops
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w = w + 1
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if v < n:
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G.add_edge(v, w)
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if v not in adjacent:
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adjacent[v] = []
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adjacent[v].append(w)
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else:
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adjacent[v].append(w)
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else:
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G = eg.Graph()
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w = -1
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lp = math.log(1.0 - p)
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v = 1
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adjacent = {}
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while v < n:
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lr = math.log(1.0 - random.random())
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w = w + 1 + int(lr / lp)
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while w >= v and v < n:
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w = w - v
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v = v + 1
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if v < n:
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G.add_edge(v, w)
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if v not in adjacent:
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adjacent[v] = []
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adjacent[v].append(w)
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else:
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adjacent[v].append(w)
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if w not in adjacent:
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adjacent[w] = []
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adjacent[w].append(v)
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else:
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adjacent[w].append(v)
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writeRandomNetworkToFile(n, adjacent, FilePath)
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return G
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def WS_Random(n, k, p, FilePath=None):
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"""Returns a small-world graph.
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Parameters
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----------
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n : int
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The number of nodes
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k : int
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Each node is joined with its `k` nearest neighbors in a ring
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topology.
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p : float
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The probability of rewiring each edge
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FilePath : string
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The file for storing the output graph G
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Returns
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-------
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G : graph
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a small-world graph
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Examples
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--------
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Returns a small-world graph G
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>>> WS_Random(100,10,0.3,"/users/fudanmsn/downloads/RandomNetwork.txt")
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"""
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if k >= n:
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print("k>=n, choose smaller k or larger n")
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return
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adjacent = {}
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G = eg.Graph()
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NUM1 = n
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NUM2 = NUM1 - 1
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K = k
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K1 = K + 1
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N = list(range(NUM1))
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G.add_nodes(N)
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for i in range(NUM1):
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for j in range(1, K1):
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K_add = NUM1 - K
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i_add_j = i + j + 1
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if i >= K_add and i_add_j > NUM1:
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i_add = i + j - NUM1
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G.add_edge(i, i_add)
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else:
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i_add = i + j
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G.add_edge(i, i_add)
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(i_add)
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else:
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adjacent[i].append(i_add)
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if i_add not in adjacent:
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adjacent[i_add] = []
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adjacent[i_add].append(i)
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else:
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adjacent[i_add].append(i)
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for i in range(NUM1):
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for e_del in range(i + 1, i + K1):
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if e_del >= NUM1:
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e_del = e_del - NUM1
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P_random = random.random()
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if P_random < p:
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G.remove_edge(i, e_del)
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adjacent[i].remove(e_del)
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if adjacent[i] == []:
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adjacent.pop(i)
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adjacent[e_del].remove(i)
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if adjacent[e_del] == []:
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adjacent.pop(e_del)
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e_add = random.randint(0, NUM2)
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while e_add == i or G.has_edge(i, e_add) == True:
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e_add = random.randint(0, NUM2)
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G.add_edge(i, e_add)
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if i not in adjacent:
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adjacent[i] = []
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adjacent[i].append(e_add)
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else:
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adjacent[i].append(e_add)
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if e_add not in adjacent:
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adjacent[e_add] = []
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adjacent[e_add].append(i)
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else:
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adjacent[e_add].append(i)
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writeRandomNetworkToFile(n, adjacent, FilePath)
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return G
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def writeRandomNetworkToFile(n, adjacent, FilePath):
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if FilePath != None:
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f = open(FilePath, "w+")
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else:
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f = open("RandomNetwork.txt", "w+")
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adjacent = sorted(adjacent.items(), key=lambda d: d[0])
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for i in adjacent:
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i[1].sort()
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for j in i[1]:
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f.write(str(i[0]))
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f.write(" ")
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f.write(str(j))
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f.write("\n")
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f.close()
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def graph_Gnm(num_v: int, num_e: int):
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r"""Return a random graph with ``num_v`` vertices and ``num_e`` edges. Edges are drawn uniformly from the set of possible edges.
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Args:
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``num_v`` (``int``): The Number of vertices.
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``num_e`` (``int``): The Number of edges.
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Examples:
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>>> import easygraph.randomhypergraph as rh
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>>> g = rh.graph_Gnm(4, 5)
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>>> g.e
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([(1, 2), (0, 3), (2, 3), (0, 2), (1, 3)], [1.0, 1.0, 1.0, 1.0, 1.0])
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"""
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assert num_v > 1, "num_v must be greater than 1"
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assert (
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num_e < num_v * (num_v - 1) // 2
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), "the specified num_e is larger than the possible number of edges"
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v_list = list(range(num_v))
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cur_num_e, e_set = 0, set()
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while cur_num_e < num_e:
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v = random.choice(v_list)
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w = random.choice(v_list)
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if v > w:
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v, w = w, v
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if v == w or (v, w) in e_set:
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continue
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e_set.add((v, w))
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cur_num_e += 1
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g = Graph()
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g.add_nodes(list(range(0, num_v)))
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for ee in list(e_set):
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g.add_edge(ee[0], ee[1], weight=1.0)
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return g
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