CS代写 Assignment_5

Assignment_5

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import random
import itertools

import networkx as nx
import numpy as np
import community

import matplotlib.pyplot as plt

## Part 1 – Modeling the NCAA College Football 2000 Network ##
G = nx.read_gml(“football.gml”)

# Print the number of nodes and edges

1.1 Structural Properties of the Graph¶

# Plot the degree distribution using histogram.

# Find the resolution parameter that leads to highest NMI.

# Plot the inter-community connection density as heatmap.

# Print the:
# network diameter,
# characteristic path length (CPL),
# average clustering coefficient,
# transitivity, and assortativity.

1.2 Configuration Model¶

# Generate Graphs

# Plot (Histogram /Boxplot):
# network diameter,
# average clustering coefficient,
# transitivity,
# assortativity.

1.3 Stochastic Block Model Graphs¶

# Generate Graphs

# Plot (Histogram /Boxplot):
# network diameter,
# average clustering coefficient,
# transitivity,
# assortativity.

1.4 Hierarchical Random Graphs¶

dendrogram = nx.read_gml(“football-hrg.gml”)

# Generate Graphs

# Plot (Histogram /Boxplot):
# network diameter,
# average clustering coefficient,
# transitivity,
# assortativity.

# Print the average value and standard deviation for:
# the diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Configuration Model Graphs
# Print the average value and standard deviation for:
# the diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Hypothesis Tests
# Print the following
# P-Values for diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Stochastic Block Model Graphs
# Print the average value and standard deviation for:
# the diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Hypothesis Tests
# Print the following
# P-Values for diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Hierarchical Random Graphs
# Print the average value and standard deviation for:
# the diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Hypothesis Tests
# Print the following
# P-Values for diameter,
# average clustering coefficient,
# transitivity,
# assortativity

# Which model do you think best approximates the empirical network? Explain your answer.

## Part 2 – Estimate the number of nodes and edges in Slashdot dataset ##
G = nx.read_edgelist(“soc-Slashdot0902.txt”, delimiter=”\t”, create_using=nx.DiGraph)
G.remove_edges_from(nx.selfloop_edges(G))

# Part 2.1

# Part 2.2

# Part 2.3

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