def question_4():
example_network = load_graph(NETWORK_URL)
er_undirected = er_und.random_undirected_graph(1239, 0.002)
upa_graph = upa.upa_generator(1239, 3)
er_attack = fast_targeted_order(er_undirected)
upa_attack = fast_targeted_order(upa_graph)
network_attack = fast_targeted_order(example_network)
er_resilience = bfs.compute_resilience(er_undirected, er_attack)
upa_resilience = bfs.compute_resilience(upa_graph, upa_attack)
network_resilience = bfs.compute_resilience(example_network, network_attack)
# Create a new figure of size 8x6 points, using 100 dots per inch
plt.figure(figsize=(8,6), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1,1,1) #parameters: row, column, location index
plt.xlabel("Number of removed nodes")
plt.ylabel("Size of the Largest Component")
plt.title("The Resilience of Different Types of Networks - using Targeted Order")
# Generate points
X = [n for n in range(len(er_resilience))]
# Plot cosine using blue color with a continuous line of width 1 (pixels)
plt.plot(X, er_resilience, color="blue", linewidth=2.0, linestyle="-", label="ER Graph")
# Plot sine using green color with a continuous line of width 1 (pixels)
plt.plot(X, upa_resilience, color="green", linewidth=2.0, linestyle="-", label="UPA Graph")
plt.plot(X, network_resilience, color="red", linewidth=2.0, linestyle="-", label="Example Network")
# Add Legends
plt.legend(loc='upper right', frameon=False)
# Show result on screen
plt.show()
def question_1():
# THE CORRECT probability factor p for er_undirected should be 0.002
# also, I SHOULD HAVE LISTED the values of p and m
example_network = load_graph(NETWORK_URL)
er_undirected = er_und.random_undirected_graph(1239, 0.002)
upa = upa.upa_generator(1239, 3)
er_attack = random_order(er_undirected)
upa_attack = random_order(upa)
network_attack = random_order(example_network)
er_resilience = bfs.compute_resilience(er_undirected, er_attack)
upa_resilience = bfs.compute_resilience(upa, upa_attack)
network_resilience = bfs.compute_resilience(example_network,
network_attack)
# Create a new figure of size 8x6 points, using 100 dots per inch
plt.figure(figsize=(10, 6), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1) #parameters: row, column, location index
plt.xlabel("Number of removed nodes")
plt.ylabel("Size of the Largest Component")
plt.title("The Resilience of Different Types of Networks")
# Generate points
X = [n for n in range(len(er_resilience))]
# Plot cosine using blue color with a continuous line of width 1 (pixels)
plt.plot(X,
er_resilience,
color="blue",
linewidth=2.0,
linestyle="-",
label="ER Graph")
# Plot sine using green color with a continuous line of width 1 (pixels)
plt.plot(X,
upa_resilience,
color="green",
linewidth=2.0,
linestyle="-",
label="UPA Graph")
plt.plot(X,
network_resilience,
color="red",
linewidth=2.0,
linestyle="-",
label="Example Network")
# Add Legends
plt.legend(loc='upper right', frameon=False)
# Show result on screen
plt.show()
def question_2():
graph_list = []
# generate UPA graphs with n in range(10, 1000) and m = 5
for num_nodes in range(10, 1000, 10):
graph_list.append(upa.upa_generator(num_nodes, 5))
num_nodes = [idx for idx in range(10, 1000, 10)]
time_fast_target = []
time_normal_target = []
for each_graph in graph_list:
start_time = time.time()
fast_targeted_order(each_graph)
elapsed_time = time.time() - start_time
time_fast_target.append(elapsed_time)
start_time = time.time()
targeted_order(each_graph)
elapsed_time = time.time() - start_time
time_normal_target.append(elapsed_time)
# Plot data
# Create a new figure of size 8x6 points, using 100 dots per inch
plt.figure(figsize=(8, 8), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1) #parameters: row, column, location index
plt.xlabel("Number of Nodes")
plt.ylabel("Time Elapsed")
plt.title("Comparasion of Two Target-finder Methods (on Desktop)")
# Plot cosine using blue color with a continuous line of width 1 (pixels)
plt.plot(num_nodes,
time_fast_target,
color="blue",
linewidth=2.0,
linestyle="-",
label="Fast Targeted Order")
# Plot sine using green color with a continuous line of width 1 (pixels)
plt.plot(num_nodes,
time_normal_target,
color="green",
linewidth=2.0,
linestyle="-",
label="(Normal) Targeted Order")
# Add Legends
plt.legend(loc='upper left', frameon=False)
# Show result on screen
plt.show()
def question_2():
graph_list = []
# generate UPA graphs with n in range(10, 1000) and m = 5
for num_nodes in range(10, 1000, 10):
graph_list.append(upa.upa_generator(num_nodes, 5))
num_nodes = [idx for idx in range(10, 1000, 10)]
time_fast_target = []
time_normal_target = []
for each_graph in graph_list:
start_time = time.time()
fast_targeted_order(each_graph)
elapsed_time = time.time() - start_time
time_fast_target.append(elapsed_time)
start_time = time.time()
targeted_order(each_graph)
elapsed_time = time.time() - start_time
time_normal_target.append(elapsed_time)
# Plot data
# Create a new figure of size 8x6 points, using 100 dots per inch
plt.figure(figsize=(8,8), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1,1,1) #parameters: row, column, location index
plt.xlabel("Number of Nodes")
plt.ylabel("Time Elapsed")
plt.title("Comparasion of Two Target-finder Methods (on Desktop)")
# Plot cosine using blue color with a continuous line of width 1 (pixels)
plt.plot(num_nodes, time_fast_target, color="blue", linewidth=2.0, linestyle="-", label="Fast Targeted Order")
# Plot sine using green color with a continuous line of width 1 (pixels)
plt.plot(num_nodes, time_normal_target, color="green", linewidth=2.0, linestyle="-", label="(Normal) Targeted Order")
# Add Legends
plt.legend(loc='upper left', frameon=False)
# Show result on screen
plt.show()
