# coding: utf-8

# ---

#

# _You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-social-network-analysis/resources/yPcBs) course resource._

#

# ---

# # Assignment 2 - Network Connectivity

#

# In this assignment you will go through the process of importing and analyzing an internal email communication network between employees of a mid-sized manufacturing company.

# Each node represents an employee and each directed edge between two nodes represents an individual email. The left node represents the sender and the right node represents the recipient.

# In[1]:

import networkx as nx

# This line must be commented out when submitting to the autograder

#!head email_network.txt

# ### Question 1

#

# Using networkx, load up the directed multigraph from `email_network.txt`. Make sure the node names are strings.

#

# *This function should return a directed multigraph networkx graph.*

# In[2]:

def answer_one():

return G

answer_one()

# ### Question 2

#

# How many employees and emails are represented in the graph from Question 1?

#

# *This function should return a tuple (#employees, #emails).*

# In[3]:

def answer_two():

return len(H),len(I)

answer_two()

# ### Question 3

#

# * Part 1. Assume that information in this company can only be exchanged through email.

#

# When an employee sends an email to another employee, a communication channel has been created, allowing the sender to provide information to the receiver, but not vice versa.

#

# Based on the emails sent in the data, is it possible for information to go from every employee to every other employee?

#

#

# * Part 2. Now assume that a communication channel established by an email allows information to be exchanged both ways.

#

# Based on the emails sent in the data, is it possible for information to go from every employee to every other employee?

#

#

# *This function should return a tuple of bools (part1, part2).*

# In[4]:

def answer_three():

return part1,part2

answer_three()

# ### Question 4

#

# How many nodes are in the largest (in terms of nodes) weakly connected component?

#

# *This function should return an int.*

# In[5]:

def answer_four():

return len(l)

answer_four()

# ### Question 5

#

# How many nodes are in the largest (in terms of nodes) strongly connected component?

#

# *This function should return an int*

# In[6]:

def answer_five():

return len(l)

answer_five()

# ### Question 6

#

# Using the NetworkX function strongly_connected_component_subgraphs, find the subgraph of nodes in a largest strongly connected component.

# Call this graph G_sc.

#

# *This function should return a networkx MultiDiGraph named G_sc.*

# In[7]:

def answer_six():

return G_sc

answer_six()

# ### Question 7

#

# What is the average distance between nodes in G_sc?

#

# *This function should return a float.*

# In[8]:

def answer_seven():

return avg_len

answer_seven()

# ### Question 8

#

# What is the largest possible distance between two employees in G_sc?

#

# *This function should return an int.*

# In[9]:

def answer_eight():

return largest_distance

answer_eight()

# ### Question 9

#

# What is the set of nodes in G_sc with eccentricity equal to the diameter?

#

# *This function should return a set of the node(s).*

# In[10]:

def answer_nine():

return set(set_of_nodes)

answer_nine()

# ### Question 10

#

# What is the set of node(s) in G_sc with eccentricity equal to the radius?

#

# *This function should return a set of the node(s).*

# In[11]:

def answer_ten():

return set(set_of_nodes)

answer_ten()

# ### Question 11

#

# Which node in G_sc is connected to the most other nodes by a shortest path of length equal to the diameter of G_sc?

#

# How many nodes are connected to this node?

#

#

# *This function should return a tuple (name of node, number of satisfied connected nodes).*

# In[17]:

def answer_eleven():

return result_node, max_count

answer_eleven()

# ### Question 12

#

# Suppose you want to prevent communication from flowing to the node that you found in the previous question from any node in the center of G_sc, what is the smallest number of nodes you would need to remove from the graph (you're not allowed to remove the node from the previous question or the center nodes)?

#

# *This function should return an integer.*

# In[18]:

def answer_twelve():

return len(nx.minimum_node_cut(G, center, node))

answer_twelve()

# ### Question 13

#

# Construct an undirected graph G_un using G_sc (you can ignore the attributes).

#

# *This function should return a networkx Graph.*

# In[14]:

def answer_thirteen():

return G_un

answer_thirteen()

# ### Question 14

#

# What is the transitivity and average clustering coefficient of graph G_un?

#

# *This function should return a tuple (transitivity, avg clustering).*

# In[15]:

def answer_fourteen():

return transitivity, avg_cluster

answer_fourteen()

# In[ ]: