def add_edge_to_graph(G, U, M, num, val):
# Adding an edge to the exisiting Graph.
for x in range(num):
u = get_random_user(U)
# val = random()
for m in M:
if (m.cumulative_prob > val):
G.insert_edge(u, m)
cumulative_prob_dist(M)
break
def add_new_movie(G, U, M, num, val):
newM = Node(0, 'M' + str(len(M)))
# Add a new Object
for x in range(num):
for u in U:
# Movie will be attached with the most preferable user.
if (u.cumulative_prob > val):
M.append(newM)
G.insert_edge(u, newM)
cumulative_prob_dist(U)
break
def add_new_user(G, U, M, num, val):
# should add this user to m nodes from M
# preserving the preferencial attachment behavior
user = Node(0, 'U' + str(len(U)))
for x in range(num):
for m in M: # 1000 * 50,000,000
# val (is a random value) will fit into the bigger span of cumulative probability line.
if (m.cumulative_degree > val):
U.append(user)
G.insert_edge(user, m)
cumulative_prob_dist(M)
break
def BA_Model(M, U, G):
for i in range(10000):
u = get_random_user(U)
for m in M:
if (m.cumulative_prob > random()):
# add an edge to k
newU = Node(0, 'U' + str(len(U)))
U.append(newU)
G.insert_edge(newU, m)
cumulative_prob_dist(M)
break
else:
G.insert_edge(u, m)
cumulative_prob_dist(M)
def add_edge_with_pref(G, U, M, num, val):
# Adding an edge with pref prob
tempM = None
tempU = None
for x in range(num):
for m in M:
if (m.cumulative_prob > val):
tempM = m
break
for u in U:
if (u.cumulative_prob > val):
tempU = u
break
G.insert_edge(tempU, tempM)
cumulative_prob_dist(M)
cumulative_prob_dist(U)
# Now there isn't a competition. So Movies can be viewed by infinite number of users.
# This is against the given definition of Zipf's Law, which states there is scarcity of the sharing resources.
# But for our scenario we have used time as a scarce resources.
# Now create a Graph with set of nodes connected in a bipartite way
G = Graph(M, U)
create_init_graph(M, U, G)
G.print_degree_dist(M)
L_m = list()
# if degree of U is 0, then it will be connected to the most degree node.
for i in range (20000) : # we are going to do operations 100 times to see what will happen to the network
# get a random user. For this I didn't consider the probabilities of User Nodes.
u = get_random_user(U)
if (u.degree in range(0,3)) : # User is a new one. If this is the case.
# connect u with one of the most degree M nodes.
cumulative_prob_dist(M)
for m in M :
if (m.cum_prob > random()) :
G.insert_edge(m, u)
cumulative_prob_dist(M)
break
else:
# now this can connect to any other node.
print("Else")
m = get_lower_degree_Node(L_m, M, 10)
G.insert_edge(m, u)
cumulative_prob_dist(M)
# if (i % 2000 == 0) :
# plot_degree_distibution(plt, M, next(colors))