wehub-resource-sync
234 lines
6.2 KiB
Python
234 lines
6.2 KiB
Python
import easygraph as eg
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__all__ = [
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"plot_Followers",
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"plot_Connected_Communities",
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"plot_Betweenness_Centrality",
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"plot_Neighborhood_Followers",
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]
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# Number of Followers
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def plot_Followers(G, SHS):
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"""
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Returns the CDF curves of "Number of Followers" of SH spanners and ordinary users in graph G.
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Parameters
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----------
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G : graph
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A easygraph graph.
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SHS : list
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The SH Spanners in graph G.
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Returns
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-------
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plt : CDF curves
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the CDF curves of "Number of Followers" of SH spanners and ordinary users in graph G.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import statsmodels.api as sm
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assert len(SHS) < len(
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G.nodes
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), "The number of SHS must be less than the number of nodes in the graph."
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OU = []
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for i in G:
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if i not in SHS:
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OU.append(i)
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degree = G.degree()
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sample1 = []
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sample2 = []
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for i in degree.keys():
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if i in OU:
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sample1.append(degree[i])
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elif i in SHS:
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sample2.append(degree[i])
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X1 = np.linspace(min(sample1), max(sample1))
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ecdf = sm.distributions.ECDF(sample1)
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Y1 = ecdf(X1)
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X2 = np.linspace(min(sample2), max(sample2))
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ecdf = sm.distributions.ECDF(sample2)
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Y2 = ecdf(X2)
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plt.plot(X1, Y1, "b--", label="Ordinary User")
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plt.plot(X2, Y2, "r", label="SH Spanner")
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plt.title("Number of Followers")
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plt.xlabel("Number of Followers")
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plt.ylabel("Cumulative Distribution Function")
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plt.legend(loc="lower right")
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plt.show()
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# Number of Connected Communities
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def plot_Connected_Communities(G, SHS):
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"""
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Returns the CDF curves of "Number of Connected Communities" of SH spanners and ordinary users in graph G.
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Parameters
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----------
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G : graph
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A easygraph graph.
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SHS : list
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The SH Spanners in graph G.
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Returns
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-------
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plt : CDF curves
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the CDF curves of "Number of Connected Communities" of SH spanners and ordinary users in graph G.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import statsmodels.api as sm
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OU = []
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for i in G:
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if i not in SHS:
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OU.append(i)
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sample1 = []
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sample2 = []
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cmts = eg.LPA(G)
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for i in OU:
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s = set()
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neighbors = G.neighbors(node=i)
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for j in neighbors:
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for k in cmts:
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if j in cmts[k]:
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s.add(k)
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sample1.append(len(s))
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for i in SHS:
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s = set()
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neighbors = G.neighbors(node=i)
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for j in neighbors:
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for k in cmts:
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if j in cmts[k]:
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s.add(k)
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sample2.append(len(s))
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print(len(cmts))
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print(sample1)
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print(sample2)
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X1 = np.linspace(min(sample1), max(sample1))
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ecdf = sm.distributions.ECDF(sample1)
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Y1 = ecdf(X1)
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X2 = np.linspace(min(sample2), max(sample2))
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ecdf = sm.distributions.ECDF(sample2)
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Y2 = ecdf(X2)
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plt.plot(X1, Y1, "b--", label="Ordinary User")
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plt.plot(X2, Y2, "r", label="SH Spanner")
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plt.title("Number of Connected Communities")
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plt.xlabel("Number of Connected Communities")
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plt.ylabel("Cumulative Distribution Function")
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plt.legend(loc="lower right")
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plt.show()
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# Betweenness Centrality
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def plot_Betweenness_Centrality(G, SHS):
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"""
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Returns the CDF curves of "Betweenness Centralitys" of SH spanners and ordinary users in graph G.
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Parameters
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----------
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G : graph
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A easygraph graph.
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SHS : list
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The SH Spanners in graph G.
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Returns
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-------
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plt : CDF curves
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the CDF curves of "Betweenness Centrality" of SH spanners and ordinary users in graph G.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import statsmodels.api as sm
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OU = []
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for i in G:
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if i not in SHS:
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OU.append(i)
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bc = eg.betweenness_centrality(G)
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bc = dict(zip(G.nodes, bc))
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sample1 = []
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sample2 = []
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for i in bc.keys():
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if i in OU:
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sample1.append(bc[i])
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else:
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sample2.append(bc[i])
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X1 = np.linspace(min(sample1), max(sample1))
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ecdf = sm.distributions.ECDF(sample1)
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Y1 = ecdf(X1)
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X2 = np.linspace(min(sample2), max(sample2))
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ecdf = sm.distributions.ECDF(sample2)
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Y2 = ecdf(X2)
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plt.plot(X1, Y1, "b--", label="Ordinary User")
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plt.plot(X2, Y2, "r", label="SH Spanner")
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plt.title("Betweenness Centrality")
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plt.xlabel("Betweenness Centrality")
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plt.ylabel("Cumulative Distribution Function")
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plt.legend(loc="lower right")
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plt.show()
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# Arg. Number of Followers of the Neighborhood Users
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def plot_Neighborhood_Followers(G, SHS):
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"""
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Returns the CDF curves of "Arg. Number of Followers of the Neighborhood Users" of SH spanners and ordinary users in graph G.
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Parameters
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----------
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G : graph
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A easygraph graph.
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SHS : list
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The SH Spanners in graph G.
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Returns
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-------
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plt : CDF curves
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the CDF curves of "Arg. Number of Followers of the Neighborhood Users
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" of SH spanners and ordinary users in graph G.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import statsmodels.api as sm
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OU = []
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for i in G:
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if i not in SHS:
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OU.append(i)
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sample1 = []
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sample2 = []
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degree = G.degree()
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for i in OU:
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num = 0
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sum = 0
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for neighbor in G.neighbors(node=i):
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num = num + 1
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sum = sum + degree[neighbor]
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sample1.append(sum / num)
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for i in SHS:
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num = 0
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sum = 0
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for neighbor in G.neighbors(node=i):
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num = num + 1
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sum = sum + degree[neighbor]
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sample2.append(sum / num)
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X1 = np.linspace(min(sample1), max(sample1))
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ecdf = sm.distributions.ECDF(sample1)
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Y1 = ecdf(X1)
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X2 = np.linspace(min(sample2), max(sample2))
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ecdf = sm.distributions.ECDF(sample2)
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Y2 = ecdf(X2)
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plt.plot(X1, Y1, "b--", label="Ordinary User")
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plt.plot(X2, Y2, "r", label="SH Spanner")
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plt.title("Arg. Number of Followers of the Neighborhood Users")
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plt.xlabel("Arg. Number of Followers of the Neighborhood Users")
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plt.ylabel("Cumulative Distribution Function")
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plt.legend(loc="lower right")
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plt.show()
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