#!/usr/bin/env python
# coding: utf-8

# # Node Centrality in SAN

# ### - Degree Centrality

# In[2]:


import networkx as nx
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

g = nx.Graph()
g.add_edge(1,2)
g.add_edge(1,3)
g.add_edge(1,4)
g.add_edge(2,3)
g.add_edge(3,4)
g.add_edge(4,5)
g.add_edge(4,6)
g.add_edge(5,6)
g.add_edge(5,7)
g.add_edge(5,8)
g.add_edge(6,7)
g.add_edge(6,8)
g.add_edge(7,8)
g.add_edge(7,9)
nx.draw(g, node_size=500, with_labels=True, font_size=16)
plt.show()


# In[3]:


degree_centrality = nx.degree_centrality(g)


# In[4]:


degree_centrality 


# In[5]:


type(degree_centrality)


# In[6]:


print degree_centrality.items()


# In[7]:


import operator

def sort_map(map):
    sortedList = map.items()
    sortedList.sort(key=operator.itemgetter(1), reverse=True)
    return sortedList


# In[8]:


sorted_degree_centrality = sort_map(degree_centrality)


# In[9]:


sorted_degree_centrality


# ### - Closeness  Centrality

# In[10]:


closeness_centrality = nx.closeness_centrality(g)


# In[11]:


closeness_centrality


# In[12]:


sorted_closeness_centrality = sort_map(closeness_centrality)


# In[13]:


sorted_closeness_centrality 


# ### - Betweenness  Centrality

# In[14]:


bet_centrality = nx.betweenness_centrality(g)


# In[15]:


bet_centrality


# In[16]:


sorted_bet_centrality =sort_map(bet_centrality)


# In[17]:


sorted_bet_centrality


# ### - Eigenvector Centrality

# In[18]:


eigenvector_centrality = nx.eigenvector_centrality(g)


# In[19]:


eigenvector_centrality


# In[20]:


sorted_eigenvector_centrality= sort_map(eigenvector_centrality)


# In[21]:


sorted_eigenvector_centrality 


# ### - Build a table with four centralities 
# 

# In[22]:


rounded_degree_centrality = {k: round(v, 3) for k, v in degree_centrality.items()}


# In[23]:


rounded_closeness_centrality = {k: round(v, 3) for k, v in closeness_centrality.items()}


# In[24]:


rounded_bet_centrality = {k: round(v, 3) for k, v in bet_centrality.items()}


# In[25]:


rounded_eigenvector_centrality = {k: round(v, 3) for k, v in eigenvector_centrality.items()}


# In[26]:


table = [[node, rounded_degree_centrality[node], 
          rounded_closeness_centrality[node], 
          rounded_bet_centrality[node], 
          rounded_eigenvector_centrality[node]] for node in g.nodes()]


# In[27]:


table