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# Attribute enrichment of the nodes
node_attr = pd.read_csv('/content/quakers_nodelist.csv', index_col = 'Name')
node_attr
# Add the atribute to the graph G
node_attr_d = node_attr.to_dict(orient = 'index')
nx.classes.function.set_node_attributes(G, node_attr_d)
nx.get_node_attributes(G, 'age')

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# Attribute enrichment of the nodes
node_attr = pd.read_csv('/content/quakers_nodelist.csv', index_col = 'Name')
node_attr
# Add the atribute to the graph G
node_attr_d = node_attr.to_dict(orient = 'index')
nx.classes.function.set_node_attributes(G, node_attr_d)
nx.get_node_attributes(G, 'age')

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pr = nx.pagerank(G)
# Plot the the graph again with Page Rank
plt.figure(figsize=(25,18))
nx.draw(G, edge_color = '#ced7d9',width = 4, arrowsize = 30, node_color = c_values, font_size =8, 
        alpha = .5, with_labels= True, node_size = [10000*pr[n] for n in G.nodes()])

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bc = nx.centrality.betweenness.betweenness_centrality(G)
bc
# Plot the the graph again with the Between Centrality
plt.figure(figsize=(25,18))
nx.draw(G, edge_color = '#ced7d9',width = 4, arrowsize = 30, node_color = c_values, font_size =8, 
        alpha = .5, with_labels= True, node_size = [10000*bc[n] for n in G.nodes()])

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# Import the packages
import community
from importlib import reload
# reload(community)
# or use Girvan-Newman algorithm
# Assgin the community function to c_values
communities = community.best_partition(G.to_undirected(), resolution = .75)
c_values = [communities.get(node) for node in G.nodes()]

# Plot the the graph again with community using c_values as color

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# Add information of people 
plt.figure(figsize=(22,16))
nx.draw(G, edge_color = '#ced7d9',width = 4, arrowsize = 30, with_labels= True)

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G = nx.from_pandas_edgelist(df,
                             source='Source',
                             target='Target',
                             create_using=nx.DiGraph()
                            )
print(nx.info(G))
# Check the closeness of the network
nx.average_clustering(G)

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# Create an empty graph
G = nx.Graph()
# Import the dataset as a dataframe
df = pd.read_csv('/content/edgelist.csv')
# Check the dimension of the data set
print(df.shape)
df

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import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
#from utils.pos import pos
from IPython.display import Image 

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pip install networkx
pip install IPython