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finding highest centrality measure in networkx python

  • Last Update :
  • Techknowledgy :

You can do it this way:

# Imports and graph creation(you don 't need them in your function)
      import networkx as nx
      import pandas as pd

      G = nx.fast_gnp_random_graph(20, 0.1)
2._
{
   0: 0.28692699490662144,
   1: 0.26953748006379585,
   2: 0.32943469785575047,
   3: 0.28692699490662144,
   4: 0.30671506352087113,
   5: 0.26953748006379585,
   ...

Then use from_dict to create the dataframe:

df = pd.DataFrame.from_dict({
   'node': list(cc.keys()),
   'centrality': list(cc.values())
})
5._
    centrality node
    12 0.404306 12
    7 0.386728 7
    2 0.329435 2
    4 0.306715 4
    0 0.286927 0
       ...

And return the result. The full code is:

def summary(G):
   cc = nx.closeness_centrality(G)
df = pd.DataFrame.from_dict({
   'node': list(cc.keys()),
   'centrality': list(cc.values())
})
return df.sort_values('centrality', ascending = False)
{
   0: 0.28692699490662144,
   1: 0.26953748006379585,
   2: 0.32943469785575047,
   3: 0.28692699490662144,
   4: 0.30671506352087113,
   5: 0.26953748006379585,
   ...
    centrality node
    0 0.286927 0
    1 0.269537 1
    2 0.329435 2
    3 0.286927 3
    4 0.306715 4
    5 0.269537 5
       ...
    centrality node
    12 0.404306 12
    7 0.386728 7
    2 0.329435 2
    4 0.306715 4
    0 0.286927 0
       ...

Suggestion : 2

Last Updated : 21 Nov, 2019

Output:

{
   0: 0.48484848484848486,
   1: 0.2727272727272727,
   2: 0.30303030303030304,
   3: 0.18181818181818182,
   4: 0.09090909090909091,
   5: 0.12121212121212122,
   6: 0.12121212121212122,
   7: 0.12121212121212122,
   8: 0.15151515151515152,
   9: 0.06060606060606061,
   10: 0.09090909090909091,
   11: 0.030303030303030304,
   12: 0.06060606060606061,
   13: 0.15151515151515152,
   14: 0.06060606060606061,
   15: 0.06060606060606061,
   16: 0.06060606060606061,
   17: 0.06060606060606061,
   18: 0.06060606060606061,
   19: 0.09090909090909091,
   20: 0.06060606060606061,
   21: 0.06060606060606061,
   22: 0.06060606060606061,
   23: 0.15151515151515152,
   24: 0.09090909090909091,
   25: 0.09090909090909091,
   26: 0.06060606060606061,
   27: 0.12121212121212122,
   28: 0.09090909090909091,
   29: 0.12121212121212122,
   30: 0.12121212121212122,
   31: 0.18181818181818182,
   32: 0.36363636363636365,
   33: 0.5151515151515151
}

Page Rank Algorithm was developed by Google founders to measure the importance of webpages from the hyperlink network structure. Page Rank assigns a score of importance to each node. Important nodes are those with many inlinks from important pages. It mainly works for Directed Networks.

n - > Number of nodes
k - > Number of steps

All nodes have a Page Rank of 1 / n
Repeat k times:
   For node u pointing to node v, add Page Rank of u
divided by out degree of u to the Page Rank of v

For understanding Page Rank, we will consider the following Graph:

Let k = 2

Initially,
A - > 1 / 5
B - > 1 / 5
C - > 1 / 5
D - > 1 / 5
E - > 1 / 5

After first iteration,
A - > 1 / 15 + 1 / 5 = 4 / 15
B - > 1 / 5 + 1 / 5 = 2 / 5
C - > 1 / 10 + 1 / 15 = 1 / 6
D - > 1 / 10
E - > 1 / 15

After second iteration,
A - > 1 / 30 + 1 / 15 = 1 / 10
B - > 4 / 15 + 1 / 6 = 13 / 30
C - > 1 / 5 + 1 / 30 = 7 / 30
D - > 1 / 5
E - > 1 / 30

So, after 2 iterations, Page Rank is as follows:
   B > C > D > A > E

Suggestion : 3

Attached below is my code and I could not anycodings_networkx figure out how to complete it. How can I anycodings_networkx find the highest centrality without having anycodings_networkx another function?,You can do it this way:,And then sort it by centrality with anycodings_networkx descending order:,How do I detect when the iPhone goes into landscape mode via JavaScript? Is there an event for this?

Attached below is my code and I could not anycodings_networkx figure out how to complete it. How can I anycodings_networkx find the highest centrality without having anycodings_networkx another function?

def summary(G):
   df = pd.DataFrame()
dc = nx.degree_centrality(G)
cc = nx.closeness_centrality(G)
bc = nx.closeness_centrality(G)
df['Nodes with the highest centrality measure'] = #addcodehere
df['Value of the highest centrality measure'] = #addcodehere
return df.set_index(['dc', 'cc', 'bc'])

You can do it this way:

# Imports and graph creation(you don 't need them in your function)
      import networkx as nx
      import pandas as pd

      G = nx.fast_gnp_random_graph(20, 0.1)
2._
{
   0: 0.28692699490662144,
   1: 0.26953748006379585,
   2: 0.32943469785575047,
   3: 0.28692699490662144,
   4: 0.30671506352087113,
   5: 0.26953748006379585,
   ...

Then use from_dict to create the anycodings_networkx dataframe:

df = pd.DataFrame.from_dict({
   'node': list(cc.keys()),
   'centrality': list(cc.values())
})
5._
    centrality node
    12 0.404306 12
    7 0.386728 7
    2 0.329435 2
    4 0.306715 4
    0 0.286927 0
       ...

And return the result. The full code is:

def summary(G):
   cc = nx.closeness_centrality(G)
df = pd.DataFrame.from_dict({
   'node': list(cc.keys()),
   'centrality': list(cc.values())
})
return df.sort_values('centrality', ascending = False)
{
   0: 0.28692699490662144,
   1: 0.26953748006379585,
   2: 0.32943469785575047,
   3: 0.28692699490662144,
   4: 0.30671506352087113,
   5: 0.26953748006379585,
   ...
    centrality node
    0 0.286927 0
    1 0.269537 1
    2 0.329435 2
    3 0.286927 3
    4 0.306715 4
    5 0.269537 5
       ...
    centrality node
    12 0.404306 12
    7 0.386728 7
    2 0.329435 2
    4 0.306715 4
    0 0.286927 0
       ...

Suggestion : 4

July 17, 2017 

import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

#
for Notebook
   %
   matplotlib inline
def draw(G, pos, measures, measure_name):

   nodes = nx.draw_networkx_nodes(G, pos, node_size = 250, cmap = plt.cm.plasma,
      node_color = list(measures.values()),
      nodelist = measures.keys())
nodes.set_norm(mcolors.SymLogNorm(linthresh = 0.01, linscale = 1, base = 10))
# labels = nx.draw_networkx_labels(G, pos)
edges = nx.draw_networkx_edges(G, pos)

plt.title(measure_name)
plt.colorbar(nodes)
plt.axis('off')
plt.show()
G = nx.karate_club_graph()
pos = nx.spring_layout(G, seed = 675)
DiG = nx.DiGraph()
DiG.add_edges_from([(2, 3), (3, 2), (4, 1), (4, 2), (5, 2), (5, 4),
   (5, 6), (6, 2), (6, 5), (7, 2), (7, 5), (8, 2),
   (8, 5), (9, 2), (9, 5), (10, 5), (11, 5)
])
dpos = {
   1: [0.1, 0.9],
   2: [0.4, 0.8],
   3: [0.8, 0.9],
   4: [0.15, 0.55],
   5: [0.5, 0.5],
   6: [0.8, 0.5],
   7: [0.22, 0.3],
   8: [0.30, 0.27],
   9: [0.38, 0.24],
   10: [0.7, 0.3],
   11: [0.75, 0.35]
}
draw(G, pos, nx.degree_centrality(G), 'Degree Centrality')
draw(DiG, dpos, nx.in_degree_centrality(DiG), 'DiGraph Degree Centrality')

Suggestion : 5

For the sake of simplicity, we removed any nodes that are not connected to any others from the dataset before we began. This was simply to reduce clutter, but it’s also very common to see lots of these single nodes in your average network dataset. ↩, For the sake of simplicity, we removed any nodes that are not connected to any others from the dataset before we began. This was simply to reduce clutter, but it’s also very common to see lots of these single nodes in your average network dataset. ↩ ,When you start work on a new dataset, it’s a good idea to get a general sense of the data. The first step, described above, is to simply open the files and see what’s inside. Because it’s a network, you know there will be nodes and edges, but how many of each are there? What information is appended to each node or edge?,As always, you can combine these measures with others. For example, here’s how you find the highest eigenvector centrality nodes in modularity class 0 (the first one):

Name, Historical Significance, Gender, Birthdate, Deathdate, ID
Joseph Wyeth, religious writer, male, 1663, 1731, 10013191
Alexander Skene of Newtyle, local politician and author, male, 1621, 1694, 10011149
James Logan, colonial official and scholar, male, 1674, 1751, 10007567
Dorcas Erbery, Quaker preacher, female, 1656, 1659, 10003983
Lilias Skene, Quaker preacher and poet, male, 1626, 1697, 10011152
Source, Target
George Keith, Robert Barclay
George Keith, Benjamin Furly
George Keith, Anne Conway Viscountess Conway and Killultagh
George Keith, Franciscus Mercurius van Helmont
George Keith, William Penn
pip3 install networkx == 2.4
import csv
from operator
import itemgetter
import networkx as nx
from networkx.algorithms
import community #This part of networkx,
   for community detection, needs to be imported separately.
with open('quakers_nodelist.csv', 'r') as nodecsv: # Open the file
nodereader = csv.reader(nodecsv) # Read the csv
# Retrieve the data(using Python list comprhension and list slicing to remove the header row, see footnote 3)
nodes = [n
   for n in nodereader
][1: ]

node_names = [n[0]
   for n in nodes
] # Get a list of only the node names

with open('quakers_edgelist.csv', 'r') as edgecsv: # Open the file
edgereader = csv.reader(edgecsv) # Read the csv
edges = [tuple(e) for e in edgereader][1: ] # Retrieve the data
print(len(node_names))


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