s444409/Network_attack_propagation
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Add weighted star experiment

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This commit is contained in:
Marcin Kostrzewski 2022-06-21 17:00:18 +02:00
parent 8ee201b319
commit de0560d538
1 changed files with 62 additions and 31 deletions
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network_attack_propagation.py
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@ -51,6 +51,9 @@ class Graph:
nodes.add(edge.node_b)
return nodes
def get_edges_with_node(self, node: Node):
return filter(lambda ed: ed.has_node(node), self.edges)
def clear_attacked(self) -> None:
for edge in self.edges:
edge.node_a.under_attack = False
@ -61,7 +64,7 @@ class Graph:
:param node: Node to search for
:return: An array of tuples (node, weight)
"""
edges_with_node = filter(lambda ed: ed.has_node(node), self.edges)
edges_with_node = self.get_edges_with_node(node)
nodes = set()
for e in edges_with_node:
if e.node_a is node:
@ -164,7 +167,7 @@ def bus_network(n=30, infected_idx=0) -> tuple[Graph, float, int]:
return network, degree_avg(edges), n
def star_network(cluster_count=5, starsize=6) -> tuple[Graph, float, int]:
def star_network(cluster_count=5, starsize=6, use_weights=False) -> tuple[Graph, float, int]:
node_count = cluster_count + cluster_count * starsize + 1
nodes = [Node() for _ in range(node_count)]
nodes[starsize-1].is_infected = True
@ -172,7 +175,8 @@ def star_network(cluster_count=5, starsize=6) -> tuple[Graph, float, int]:
edges = []
for x in range(cluster_count):
center_node = x * starsize + x
edges += [Edge(nodes[center_node], nodes[i], 1.0) for i in range(center_node + 1, center_node + starsize + 1)]
vulnerability = 1.0 if not use_weights else max(1.0, starsize)
edges += [Edge(nodes[center_node], nodes[i], vulnerability) for i in range(center_node + 1, center_node + starsize + 1)]
edges.append(Edge(nodes[-1], nodes[center_node], 1.0))
network = Graph()
@ -201,48 +205,75 @@ def summary(average_degrees: list[float], propagation_speeds: list[float]) -> No
plt.show()
def experiment(network, avg_degree, node_count, iteration):
def bus_experiment():
degrees = []
speeds = []
do_graph_animation(f'bus{iteration}.gif', network, 90, nx.spiral_layout)
speeds.append(node_count / network.rounds_survived)
degrees.append(avg_degree)
print(f"\n{node_count} NODE BUS")
print(f"average degree = {avg_degree}")
sizes = [0, 8, 15]
for i in sizes:
bus, bus_avg_degree, node_count = bus_network(20 + i)
do_graph_animation('bus{i}.gif', bus, 90, nx.spring_layout)
speeds.append(bus.rounds_survived / node_count)
degrees.append(bus_avg_degree)
print(f"\n{node_count} NODE bus")
print(f"average degree = {bus_avg_degree}")
print(f"propagation speed = {round(speeds[-1], 2)}")
print(f"bus_{iteration} rounds survived = {network.rounds_survived + 1}")
print(f"bus{i} rounds survived = {bus.rounds_survived + 1}")
summary(degrees, speeds)
def ring_experiment():
degrees = []
speeds = []
sizes = [0, 8, 15]
for i in sizes:
ring, ring_avg_degree, node_count = ring_network(20 + i)
do_graph_animation('bus{i}.gif', ring, 90, nx.circular_layout)
speeds.append(ring.rounds_survived / node_count)
degrees.append(ring_avg_degree)
print(f"\n{node_count} NODE ring")
print(f"average degree = {ring_avg_degree}")
print(f"propagation speed = {round(speeds[-1], 2)}")
print(f"ring{i} rounds survived = {ring.rounds_survived + 1}")
def star_experiment():
degrees = []
speeds = []
sizes = range(0, 8, 2)
for i in sizes:
star, star_avg_degree, node_count = star_network(cluster_count=3 + i, starsize=3 + i)
do_graph_animation(f'star{i}.gif', star, 120, nx.kamada_kawai_layout)
speeds.append(star.rounds_survived / node_count)
degrees.append(star_avg_degree)
print(f"\n{node_count} NODE STAR")
print(f"average degree = {star_avg_degree}")
print(f"propagation speed = {round(speeds[-1], 2)}")
print(f"star{i} rounds survived = {star.rounds_survived + 1}")
summary(degrees, speeds)
def bus_experiment():
sizes = [0, 8, 15]
for i in sizes:
bus, bus_avg_degree, node_count = bus_network(20 + i)
experiment(bus, bus_avg_degree, node_count, i)
def ring_experiment():
sizes = [0, 8, 15]
for i in sizes:
ring, ring_avg_degree, node_count = ring_network(20 + i)
experiment(ring, ring_avg_degree, node_count, i)
def star_experiment():
sizes = [0, 8, 15]
for i in sizes:
star, star_avg_degree, node_count = star_network(cluster_count=3 + i, starsize=3 + i)
experiment(star, star_avg_degree, node_count, i)
def weighted_star_experiment():
degrees = []
speeds = []
sizes = range(0, 8, 2)
for i in sizes:
star, star_avg_degree, node_count = star_network(cluster_count=3 + i, starsize=3 + i, use_weights=True)
do_graph_animation(f'star_weighted{i}.gif', star, 120, nx.kamada_kawai_layout)
speeds.append(star.rounds_survived / node_count)
degrees.append(star_avg_degree)
print(f"\n{node_count} NODE STAR")
print(f"average degree = {star_avg_degree}")
print(f"propagation speed = {round(speeds[-1], 2)}")
print(f"star{i} rounds survived = {star.rounds_survived + 1}")
summary(degrees, speeds)
def main():
bus_experiment()
ring_experiment()
star_experiment()
weighted_star_experiment()
if __name__ == "__main__":