# coding: utf-8

# # Report

# In[77]:

#imports

import matplotlib.pyplot as plt

import networkx as nx

import seaborn as sns

import heapq

from heapq import heappush, heappop

import itertools

import datetime as d

import Modules

# In[21]:

#Open file

fo = open('D:/Università/Data Science/ADM/HW4/full_dblp.json', 'r')

data = fo.read()

fo.close()

import json

dataset = json.loads(data)

# In[24]:

#Create dictionaries

authors_dict, authors_dict_reference, publications_dict, conferences_dict=Modules.createDict(dataset)

# In[25]:

#Create the graph and the dictionary with similar nodes

G, similar=Modules.createGraph(authors_dict, publications_dict)

# In[26]:

nx.info(G)

# ## 2a.

# In[27]:

#Starting from the original graph, create the subgraph for the conference in input

G2a = G.copy()

conf = input("Insert a conference name: ")

for node in nx.nodes(G):

for tup in G.node[node]['conferences']:

try:

if tup[0] != conf:

G2a.remove_node(node)

except:

continue

print("Done!")

# In[28]:

nx.info(G2a)

# In[41]:

'''Compute the degree for nodes which is the number of edges each node has.'''

dg_values=list(nx.degree(G2a))

# In[55]:

print(min(dg_values))

print(max(dg_values))

# In[44]:

#Degree histogram

sns.set_style("darkgrid")

sns.set_context({"figure.figsize": (6, 4)})

fig, ax = plt.subplots()

sns.distplot(dg_values, color="dodgerblue", bins=8, hist=True, kde=False)

plt.xlabel("Degree", fontsize=12)

plt.ylabel("Node frequences", fontsize=12)

plt.legend(prop={'size':16})

plt.title("Nodes degree", fontsize = 16)

plt.show()

# In[ ]:

'''Looking at the histogram above, we can see that the minimum degree is 881867 and the maximum is 88234.

It means that the authors in this conference (given in input) are extremely connected to the others.'''

# In[42]:

'''Compute the degree centrality for nodes.

The degree centrality for a node v is the fraction of nodes it is connected to.'''

dvalues=list(nx.degree_centrality(G2a).values())

# In[45]:

#Degree centrality histogram

sns.set_style("darkgrid")

sns.set_context({"figure.figsize": (6, 4)})

fig, ax = plt.subplots()

sns.distplot(dvalues, color="dodgerblue", bins=8, hist=True, kde=False)

plt.xlabel("Degree centrality values", fontsize=12)

plt.ylabel("Node frequences", fontsize=12)

plt.legend(prop={'size':16})

plt.title("Degree centrality", fontsize = 16)

plt.xticks((0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09))

plt.show()

# In[18]:

'''As we can see from the plot, we got high frequences for low values of the centrality.'''

# In[34]:

'''Compute the closeness centrality for nodes in a bipartite network.

The closeness of a node is the distance to all other nodes in the graph or in the case that the graph is not connected

to all other nodes in the connected component containing that node.'''

cvalues=list(nx.closeness_centrality(G2a).values())

# In[35]:

#Closeness centrality histogram

sns.set_style("darkgrid")

sns.set_context({"figure.figsize": (6, 4)})

fig, ax = plt.subplots()

sns.distplot(cvalues, color="dodgerblue", bins=8, hist=True, kde=False)

plt.xlabel("Closeness centrality values", fontsize=12)

plt.ylabel("Node frequences", fontsize=12)

plt.legend(prop={'size':16})

plt.title("Closeness centrality", fontsize = 16)

plt.xticks((0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09))

plt.show()

# In[ ]:

'''As we can see from the plot, even for Closeness centrality,

we got high frequences for values of the centrality between 0.00 and 0.01 and low frequences for other values.'''

# In[36]:

'''Compute the shortest-path betweenness centrality for nodes.

Betweenness centrality of a node vv is the sum of the fraction of all-pairs shortest paths that pass through v.'''

bvalues=list(nx.betweenness_centrality(G2a, weight="weight").values())

# In[37]:

sns.set_style("darkgrid")

sns.set_context({"figure.figsize": (6, 4)})

fig, ax = plt.subplots()

sns.distplot(bvalues, color="dodgerblue", bins=8, hist=True, kde=False)

plt.xlabel("Between centrality values", fontsize=12)

plt.ylabel("Node frequences", fontsize=12)

plt.legend(prop={'size':16})

plt.title("Between centrality", fontsize = 16)

plt.xticks(rotation=45)

plt.show()

# In[ ]:

'''In this plot we can see that values which are different from 0.00 have low frequences,

even lower than in the other plots.'''

# ## 2b.

#

# In[56]:

#Create the sub graph for the author in input at maximum hop distance given in input.

G2b = nx.Graph()

a = int(input("Enter an author id: "))

dist=int(input("Enter max hop distance: "))

G2b = G.subgraph(Modules.neighbors(G, a, dist))

print("Done!")

#256176 aris

# In[57]:

nx.info(G2b)

# In[73]:

#here we assign the color "blue" to aris' node and "red" to others

for node in G2b.nodes():

if (node == a):

color = 'green'

node_size=2000

else:

color = 'violet'

node_size=250

G2b.node[node]['color'] = color

G2b.node[node]['node_size']= node_size

# In[74]:

#plot of the subgraph

plt.clf()

plt.figure(num=None, figsize=(15,15), dpi=50)

nx.draw(G2b, node_shape= '.', node_size=list(nx.get_node_attributes(G2b,'node_size').values()), node_color = list(nx.get_node_attributes(G2b,'color').values()))

#nx.draw_networkx_edges(G2b, alpha=.5)

plt.show()

# In[63]:

#Create the graph removing similar nodes

Gcon=Modules.removeNodes(G, similar)

# In[64]:

nx.info(Gcon)

# ## 3a.

# In[75]:

p=Modules.aris_subgraph(Gcon, similar)

# In[76]:

Modules.distances_aris(p, similar)

# ## 3b.

# In[80]:

st=d.datetime.now()

groupnumber=Modules.groupNumber(G, Gcon, similar)

print("Execution time: "+str(d.datetime.now()-st))

# In[89]:

#Print the output just for the first ten nodes.

i=0

for k,v in groupnumber.items():

if i <= 10:

print("Node "+str(k)+":", v)

else:

break

i+=1

# In[ ]: