/
Centralities.py
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Centralities.py
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import collections
import matplotlib.pyplot as dia
import networkx as nx
pic = 0
def CalculateCentrality(interval, DirectedGraph, UndirectedGraph):
# DegreeCentrality(interval, G)
# ClosenessCentrality(interval, G)
# BetweennesCentrality(interval, G)
# EigenvectorCentrality(interval, G)
# KatzCentrality(interval, G)
Centrality(interval, UndirectedGraph, "DegreeCentrality")
Centrality(interval, UndirectedGraph, "ClosenessCentrality")
Centrality(interval, UndirectedGraph, "BetweennesCentrality")
Centrality(interval, UndirectedGraph, "EigenvectorCentrality")
Centrality(interval, UndirectedGraph, "KatzCentrality")
Centrality(interval, DirectedGraph, "InDegreeCentrality")
Centrality(interval, DirectedGraph, "OutDegreeCentrality")
def Centrality(interval, G, title):
dc = 0
label = ""
if title == "DegreeCentrality":
# dc = list(nx.degree_centrality(G).items(), key=lambda kv: kv[1])
dc = list(nx.degree_centrality(G).items())
label = "Degree Centrality"
elif title == "ClosenessCentrality":
# dc = list(nx.closeness_centrality(G).items(), key=lambda kv: kv[1])
dc = list(nx.closeness_centrality(G).items())
label = "Closeness Centrality"
elif title == "BetweennesCentrality":
# dc = list(nx.betweenness_centrality(G).items(), key=lambda kv: kv[1])
dc = list(nx.betweenness_centrality(G).items())
label = "Betweennes Centrality"
elif title == "EigenvectorCentrality":
# dc = list(nx.eigenvector_centrality_numpy(G,1000).items(), key=lambda kv: kv[1])
dc = list(nx.eigenvector_centrality_numpy(G, 1000).items())
label = "Eigenvector Centrality"
elif title == "KatzCentrality":
# dc = list(nx.katz_centrality_numpy(G).items(), key=lambda kv: kv[1])
dc = list(nx.katz_centrality_numpy(G).items())
label = "Katz Centrality"
elif title == "InDegreeCentrality":
# dc = list(nx.in_degree_centrality(G).items(), key=lambda kv: kv[1])
dc = list(nx.in_degree_centrality(G).items())
label= "In-Degree Centrality"
elif title == "OutDegreeCentrality":
# dc = list(nx.out_degree_centrality(G).items(), key=lambda kv: kv[1])
dc = list(nx.out_degree_centrality(G).items())
label= "Out-Degree Centrality"
i = 0
vals={}
for v in dc:
if v[1] in vals:
vals[v[1]]+=1
else:
vals[v[1]]=1
xlist = list(vals.keys())
ylist = list(vals.values())
dia.plot(xlist, ylist, alpha=0.8, color='gold', label='Key Value')
dia.xlabel('Centrality')
dia.ylabel('Percentage')
dia.title(interval.getMinMax() + "\n" + label)
global pic
pic += 1
dia.savefig("Centrality_Diagrams/"+label+str(pic))
# dia.show()
# def DegreeCentrality(interval, G):
# dc listsorted(nx.degree_centrality(G).items(), key=lambda kv: kv[1])
# i = 0
# tmp = next(iter(dc.values())) # the first value of the centralities
# dictToDia = dict()
# leng = 0
# for value in dc.values():
# leng += 1
# if tmp == value:
# i += 1
# # if is last element
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i/len(dc))
# if tmp != value:
# dictToDia.__setitem__(tmp, i/len(dc))
# i = 1
# tmp = value
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i/len(dc))
#
# xlist = []
# ylist = []
# for k, v in dictToDia.items():
# xlist.append(k)
# ylist.append(v)
#
# #print(xlist)
# # proti agili x, deuterh y
# # dia.plot([1, 3], [2, 5], alpha=0.8, color='black', label='Key Value')
# dia.plot( xlist, ylist, alpha=0.8, color='gold', label='Key Value')
#
# dia.xlabel('Centrality')
# dia.ylabel('Percentage')
# dia.title(interval + "\n" + "Degree Centrality")
#
# # yaxis = []
# # test = list(dictToDia.values())
# # yaxis.append(test[0])
# # if len(dictToDia)%2 == 0:
# # yaxis.append(round(test[int(len(dictToDia)/2)], 2))
# # else:
# # yaxis.append(round(test[int(len(dictToDia)/2 -1)], 2))
# # yaxis.append(round((test[len(test)-1]), 2))
# # for v in dictToDia.values():
# # str.append(v)
# # dia.xticks(yaxis)
# dia.legend()
# dia.tight_layout()
# dia.show()
#
#
# def ClosenessCentrality(interval, G):
# dc listsorted(nx.closeness_centrality(G).items(), key=lambda kv: kv[1])
# i = 0
# tmp = next(iter(dc.values())) # the first value of the centralities
# dictToDia = dict()
# leng = 0
# for value in dc.values():
# leng += 1
# if tmp == value:
# i += 1
# # if is last element
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i/len(dc))
# if tmp != value:
# dictToDia.__setitem__(tmp, i/len(dc))
# i = 1
# tmp = value
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i/len(dc))
#
# xlist = []
# ylist = []
# for k, v in dictToDia.items():
# xlist.append(k)
# ylist.append(v)
#
# #print(xlist)
# # proti agili x, deuterh y
# # dia.plot([1, 3], [2, 5], alpha=0.8, color='black', label='Key Value')
# dia.plot( xlist, ylist, alpha=0.8, color='gold', label='Key Value')
#
# dia.xlabel('Centrality')
# dia.ylabel('Percentage')
# dia.title(interval + "\n" + "Closeness Centrality")
#
# # yaxis = []
# # test = list(dictToDia.values())
# # yaxis.append(test[0])
# # if len(dictToDia)%2 == 0:
# # yaxis.append(round(test[int(len(dictToDia)/2)], 2))
# # else:
# # yaxis.append(round(test[int(len(dictToDia)/2 -1)], 2))
# # yaxis.append(round((test[len(test)-1]), 2))
# # for v in dictToDia.values():
# # str.append(v)
# #dia.xticks(yaxis)
# dia.legend()
# dia.tight_layout()
# dia.show()
#
#
# def BetweennesCentrality(interval, G):
# dc listsorted(nx.betweenness_centrality(G).items(), key=lambda kv: kv[1])
# i = 0
# tmp = next(iter(dc.values())) # the first value of the centralities
# dictToDia = dict()
# leng = 0
# for value in dc.values():
# leng += 1
# if tmp == value:
# i += 1
# # if is last element
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
# if tmp != value:
# dictToDia.__setitem__(tmp, i / len(dc))
# i = 1
# tmp = value
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
#
# xlist = []
# ylist = []
# for k, v in dictToDia.items():
# xlist.append(k)
# ylist.append(v)
#
# # print(xlist)
# # proti agili x, deuterh y
# # dia.plot([1, 3], [2, 5], alpha=0.8, color='black', label='Key Value')
# dia.plot(xlist, ylist, alpha=0.8, color='gold', label='Key Value')
#
# dia.xlabel('Centrality')
# dia.ylabel('Percentage')
# dia.title(interval + "\n" + "Betweenness Centrality")
#
# # yaxis = []
# # test = list(dictToDia.values())
# # yaxis.append(test[0])
# # if len(dictToDia) % 2 == 0:
# # yaxis.append(round(test[int(len(dictToDia) / 2)], 2))
# # else:
# # yaxis.append(round(test[int(len(dictToDia) / 2 - 1)], 2))
# # yaxis.append(round((test[len(test) - 1]), 2))
# i = 0
# # for v in dictToDia.values():
# # str.append(v)
# # print("to yaxis einai " + str(yaxis))
# # yaxis.append(min(test))
# # yaxis.append(max(test))
# #dia.xticks(yaxis)
# dia.legend()
# dia.tight_layout()
# dia.show()
#
#
# def EigenvectorCentrality(interval, G):
# dc listsorted(nx.eigenvector_centrality(G).items(), key=lambda kv: kv[1])
# i = 0
# tmp = next(iter(dc.values())) # the first value of the centralities
# dictToDia = dict()
# leng = 0
# for value in dc.values():
# leng += 1
# if tmp == value:
# i += 1
# # if is last element
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
# if tmp != value:
# dictToDia.__setitem__(tmp, i / len(dc))
# i = 1
# tmp = value
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
#
# xlist = []
# ylist = []
# for k, v in dictToDia.items():
# xlist.append(k)
# ylist.append(v)
#
# # print(xlist)
# # proti agili x, deuterh y
# # dia.plot([1, 3], [2, 5], alpha=0.8, color='black', label='Key Value')
# dia.plot(xlist, ylist, alpha=0.8, color='gold', label='Key Value')
#
# dia.xlabel('Centrality')
# dia.ylabel('Percentage')
# dia.title(interval + "\n" + "Eigenvector Centrality")
#
# # yaxis = []
# # test = list(dictToDia.values())
# # yaxis.append(test[0])
# # if len(dictToDia) % 2 == 0:
# # yaxis.append(round(test[int(len(dictToDia) / 2)], 2))
# # else:
# # yaxis.append(round(test[int(len(dictToDia) / 2 - 1)], 2))
# # yaxis.append(round((test[len(test) - 1]), 2))
# # yaxis.append(min(test))
# # yaxis.append(max(test))
# # for v in dictToDia.values():
# # str.append(v)
# # dia.xticks(yaxis)
# dia.legend()
# dia.tight_layout()
# dia.show()
#
#
# def KatzCentrality(interval, G):
#
# dc listsorted(nx.katz_centrality_numpy(G).items(), key=lambda kv: kv[1])
# i = 0
# tmp = next(iter(dc.values())) # the first value of the centralities
# dictToDia = dict()
# leng = 0
# for value in dc.values():
# leng += 1
# if tmp == value:
# i += 1
# # if is last element
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
# if tmp != value:
# dictToDia.__setitem__(tmp, i / len(dc))
# i = 1
# tmp = value
# if leng == len(dc):
# dictToDia.__setitem__(tmp, i / len(dc))
#
# xlist = []
# ylist = []
# for k, v in dictToDia.items():
# xlist.append(k)
# ylist.append(v)
#
# # print(xlist)
# # proti agili x, deuterh y
# # dia.plot([1, 3], [2, 5], alpha=0.8, color='black', label='Key Value')
# dia.plot(xlist, ylist, alpha=0.8, color='gold', label='Key Value')
#
# dia.xlabel('Centrality')
# dia.ylabel('Percentage')
# dia.title(interval + "\n" + "Katz Centrality")
#
# # yaxis = []
# # test = list(dictToDia.values())
# # yaxis.append(test[0])
# # if len(dictToDia) % 2 == 0:
# # yaxis.append(round(test[int(len(dictToDia) / 2)], 2))
# # else:
# # yaxis.append(round(test[int(len(dictToDia) / 2 - 1)], 2))
# # yaxis.append(round((test[len(test) - 1]), 2))
# # for v in dictToDia.values():
# # str.append(v)
# # print("to yaxis einai "+str(yaxis))
# # yaxis.append(min(test))
# # yaxis.append(max(test))
# # dia.xticks(yaxis)
# dia.legend()
# dia.tight_layout()
# dia.savefig("lola.png")
# dia.show()