def random_upa_graphs():
""" Implementation on random graphs of varying sizes that are
generated under the undirected preferential attachment model.
Argument:
None
Returns:
a tuple containing the result of the run and number of nodes used."""
first = provided.upa(2,2)
second = provided.upa(4,2)
third = provided.upa(6,2)
fourth = provided.upa(8,2)
fifth = provided.upa(10,2)
sixth = provided.upa(12,2)
print comp182.time_func(brute_force_distance, [first, 0]), "UPA(2,2)"
print "Edges in graph:", edge_count(first)
print comp182.time_func(brute_force_distance, [second, 0]),"UPA(4,2)"
print "Edges in graph:", edge_count(second)
print comp182.time_func(brute_force_distance, [third, 0]),"UPA(6,2)"
print "Edges in graph:", edge_count(third)
print comp182.time_func(brute_force_distance, [fourth, 0]),"UPA(8,2)"
print "Edges in graph:", edge_count(fourth)
print comp182.time_func(brute_force_distance, [fifth, 0]),"UPA(10,2)"
print "Edges in graph:", edge_count(fifth)
print comp182.time_func(brute_force_distance, [sixth, 0]),"UPA(12,2)"
print "Edges in graph:", edge_count(sixth)
def bfs_random_upa_graphs():
""" Implementation on random graphs of varying sizes that are
generated under the undirected preferential attachment model
using the BFS algorithm.
Argument:
None
Returns:
a tuple containing the result of the run and number of nodes used."""
first = provided.upa(500,5)
second = provided.upa(500,20)
third = provided.upa(500,80)
fourth = provided.upa(500,320)
print comp182.time_func(bfs, [first, 0]), "UPA(500,5)"
print "Edges in graph:", edge_count(first)
print comp182.time_func(bfs, [second, 0]),"UPA(500,20)"
print "Edges in graph:", edge_count(second)
print comp182.time_func(bfs, [third, 0]),"UPA(500,80)"
print "Edges in graph:", edge_count(third)
print comp182.time_func(bfs, [fourth, 0]),"UPA(500,320)"
print "Edges in graph:", edge_count(fourth)
def analyze_graphs():
""" Runs all six experiments and plots the pictorial graphs.
Arguments:
None
Returns:
None"""
#Analyzes the network graph and creates similar perimeters for Erdos and UPA graph
nodes = node_count_function(networktopology)
print "Nodes in Network graph:", nodes
string1, edgesavg = average_edge_per_node(networktopology)
edges = edge_count(networktopology)
print "Averge Number of Edges:", edgesavg
totaldegree = provided.total_degree(networktopology)
print "Total degree of Network graph:", totaldegree
probabilityerdos = float(edges)/(nodes*(nodes-1)/2.0)
print "Probability P for Erdos:", probabilityerdos
upa = provided.upa(nodes,edgesavg)
#Saves the grahes to be modified.
savedrandomgraph = comp182.copy_graph(upa)
erdos = provided.erdos_renyi(nodes,probabilityerdos)
savederdosgraph = comp182.copy_graph(erdos)
savednetworkgraph = comp182.copy_graph(networktopology)
#runs the different attacks on the various graphs
randomgraphattack = random_attack(comp182.copy_graph(upa))
print "First plot finished."
randomgraphtargeted = targeted_attack(comp182.copy_graph(upa))
print "Second plot finished."
erdosgraphattack = random_attack(comp182.copy_graph(erdos))
print "Third plot finished."
erdosgraphtargeted = targeted_attack(comp182.copy_graph(erdos))
print "Fourth plot finished."
networkgraphattack = random_attack(comp182.copy_graph(savednetworkgraph))
print "Fifth plot finished."
networkgraphtargeted = targeted_attack(savednetworkgraph)
print "Sixth plot finished."
#formats the pictorial graph
labelerdos = "Erdos({}, {})".format(nodes,probabilityerdos)
labelupa = "UPA({},{})".format(nodes,edgesavg)
labels = ["Network Graph"+"Targeted", "Network Graph"+"Random", labelerdos+"Targeted", labelerdos+"Random",labelupa+"Targeted", labelupa+"Random"]
comp182.plot_lines([networkgraphtargeted, networkgraphattack, erdosgraphtargeted, erdosgraphattack, randomgraphtargeted, randomgraphattack],"Resiliency of Graphs' Attacks", "Nodes Removded", "Largest Connected-Component", labels, filename="Resiliency of Graphs' Attacks")
import comp182
import provided
import connected_components
import random
network = comp182.read_graph('rf7.repr')
random1 = provided.upa(1347,5)
random2 = provided.erdos_renyi(1347, float(6224)/float(906531))
def random_remove(g):
"""
#remove one node from graph randomly
#argument:
g -- graph
#return:
a graph with a node removed
"""
random_node = random.sample(g,1)[0] # choose random node
g.pop(random_node,None) # remove node
for node in g:
if g[node] & {random_node} == {random_node}:
g[node].remove(random_node)
return g
# test for random_remove
#test1 = random_remove(provided.upa(5,2))
def plot_remove(g):
"""
#remove the node with largest cut,
plot the size of connected components
with respect to number of nodes removed.
g3 = {0: {1,2,3},
1: {0,4,5},
2: {0,3,6},
3: {0,2,7},
4: {1,3,5,8},
5: {1,4,9},
6: {2,7,10},
7: {3,6,8,10},
8: {4,7,9,11},
9: {5,8,11},
10:{6,7,11},
11:{8,9,10},
}
result1 = bfs(g1,0)
result2 = bfs(g2,0)
result3 = bfs(g3,0)
"""
# test 2 #
g4 = provided.upa(5, 5)
g5 = provided.upa(500, 20)
g6 = provided.upa(500, 80)
g7 = provided.upa(500, 320)
result4 = comp182.time_func(bfs, [g4, 0])
result5 = comp182.time_func(bfs, [g5, 0])
result6 = comp182.time_func(bfs, [g6, 0])
result7 = comp182.time_func(bfs, [g7, 0])
