社交网络中的级联行为原文:https://www . geeksforgeeks . org/级联-社交网络中的行为/先决条件:
社交网络中的级联行为
原文:https://www . geeksforgeeks . org/级联-社交网络中的行为/
先决条件: 社交网络入门Python 基础知识
当人们通过网络相互联系在一起时，他们可以影响彼此的行为和决定。这被称为网络中的级联行为。
让我们考虑一个例子，假设一个社会中的所有人都采用了一种趋势 x，现在出现了新的趋势 Y，一个小群体接受了这种新的趋势，在这之后，他们的邻居也接受了这种趋势 Y，以此类推。
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级联行为示例(a=2，b=3，p=2/5)
所以，级联行为主要有 4 个思路:
- 增加收益。
- 关键人物。
- 社区对瀑布的影响。
- 级联和集群。
下面是每个想法的代码。
1。增加收益。
Python 3
# cascade pay off
import networkx as nx
import matplotlib.pyplot as plt
def set_all_B(G):
for i in G.nodes():
G.nodes[i]['action'] = 'B'
return G
def set_A(G, list1):
for i in list1:
G.nodes[i]['action'] = 'A'
return G
def get_colors(G):
color = []
for i in G.nodes():
if (G.nodes[i]['action'] == 'B'):
color.append('red')
else:
color.append('blue')
return color
def recalculate(G):
dict1 = {}
# payoff(A)=a=4
# payoff(B)=b=3
a = 15
b = 5
for i in G.nodes():
neigh = G.neighbors(i)
count_A = 0
count_B = 0
for j in neigh:
if (G.nodes[j]['action'] == 'A'):
count_A += 1
else:
count_B += 1
payoff_A = a * count_A
payoff_B = b * count_B
if (payoff_A >= payoff_B):
dict1[i] = 'A'
else:
dict1[i] = 'B'
return dict1
def reset_node_attributes(G, action_dict):
for i in action_dict:
G.nodes[i]['action'] = action_dict[i]
return G
def Calculate(G):
terminate = True
count = 0
c = 0
while (terminate and count < 10):
count += 1
# action_dict will hold a dictionary
action_dict = recalculate(G)
G = reset_node_attributes(G, action_dict)
colors = get_colors(G)
if (colors.count('red') == len(colors) or colors.count('green') == len(colors)):
terminate = False
if (colors.count('green') == len(colors)):
c = 1
nx.draw(G, with_labels=1, node_color=colors, node_size=800)
plt.show()
if (c == 1):
print('cascade complete')
else:
print('cascade incomplete')
G = nx.erdos_renyi_graph(10, 0.5)
nx.write_gml(G, "erdos_graph.gml")
G = nx.read_gml('erdos_graph.gml')
print(G.nodes())
G = set_all_B(G)
# initial adopters
list1 = ['2', '1', '3']
G = set_A(G, list1)
colors = get_colors(G)
nx.draw(G, with_labels=1, node_color=colors, node_size=800)
plt.show()
Calculate(G)
输出:
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
cascade complete
![]()
2。关键人物。
Python 3
# cascade key people
import networkx as nx
import matplotlib.pyplot as plt
G = nx.erdos_renyi_graph(10, 0.5)
nx.write_gml(G, "erdos_graph.gml")
def set_all_B(G):
for i in G.nodes():
G.nodes[i]['action'] = 'B'
return G
def set_A(G, list1):
for i in list1:
G.nodes[i]['action'] = 'A'
return G
def get_colors(G):
color = []
for i in G.nodes():
if (G.nodes[i]['action'] == 'B'):
color.append('red')
else:
color.append('green')
return color
def recalculate(G):
dict1 = {}
# payoff(A)=a=4
# payoff(B)=b=3
a = 10
b = 5
for i in G.nodes():
neigh = G.neighbors(i)
count_A = 0
count_B = 0
for j in neigh:
if (G.nodes[j]['action'] == 'A'):
count_A += 1
else:
count_B += 1
payoff_A = a * count_A
payoff_B = b * count_B
if (payoff_A >= payoff_B):
dict1[i] = 'A'
else:
dict1[i] = 'B'
return dict1
def reset_node_attributes(G, action_dict):
for i in action_dict:
G.nodes[i]['action'] = action_dict[i]
return G
def Calculate(G):
continuee = True
count = 0
c = 0
while (continuee and count < 100):
count += 1
# action_dict will hold a dictionary
action_dict = recalculate(G)
G = reset_node_attributes(G, action_dict)
colors = get_colors(G)
if (colors.count('red') == len(colors) or colors.count('green') == len(colors)):
continuee = False
if (colors.count('green') == len(colors)):
c = 1
if (c == 1):
print('cascade complete')
else:
print('cascade incomplete')
G = nx.read_gml('erdos_graph.gml')
for i in G.nodes():
for j in G.nodes():
if (i < j):
list1 = []
list1.append(i)
list1.append(j)
print(list1, ':', end="")
G = set_all_B(G)
G = set_A(G, list1)
colors = get_colors(G)
Calculate(G)
输出:
['0', '1'] :cascade complete
['0', '2'] :cascade incomplete
['0', '3'] :cascade complete
['0', '4'] :cascade complete
['0', '5'] :cascade incomplete
['0', '6'] :cascade complete
['0', '7'] :cascade complete
['0', '8'] :cascade complete
['0', '9'] :cascade complete
['1', '2'] :cascade complete
['1', '3'] :cascade complete
['1', '4'] :cascade complete
['1', '5'] :cascade complete
['1', '6'] :cascade complete
['1', '7'] :cascade complete
['1', '8'] :cascade complete
['1', '9'] :cascade complete
['2', '3'] :cascade incomplete
['2', '4'] :cascade incomplete
['2', '5'] :cascade incomplete
['2', '6'] :cascade incomplete
['2', '7'] :cascade incomplete
['2', '8'] :cascade incomplete
['2', '9'] :cascade complete
['3', '4'] :cascade complete
['3', '5'] :cascade incomplete
['3', '6'] :cascade complete
['3', '7'] :cascade complete
['3', '8'] :cascade complete
['3', '9'] :cascade complete
['4', '5'] :cascade incomplete
['4', '6'] :cascade complete
['4', '7'] :cascade complete
['4', '8'] :cascade complete
['4', '9'] :cascade incomplete
['5', '6'] :cascade incomplete
['5', '7'] :cascade incomplete
['5', '8'] :cascade incomplete
['5', '9'] :cascade complete
['6', '7'] :cascade complete
['6', '8'] :cascade complete
['6', '9'] :cascade complete
['7', '8'] :cascade complete
['7', '9'] :cascade complete
['8', '9'] :cascade complete
3。社区对瀑布的影响。
Python 3
import networkx as nx
import random
import matplotlib.pyplot as plt
def first_community(G):
for i in range(1, 11):
G.add_node(i)
for i in range(1, 11):
for j in range(1, 11):
if (i < j):
r = random.random()
if (r < 0.5):
G.add_edge(i, j)
return G
def second_community(G):
for i in range(11, 21):
G.add_node(i)
for i in range(11, 21):
for j in range(11, 21):
if (i < j):
r = random.random()
if (r < 0.5):
G.add_edge(i, j)
return G
G = nx.Graph()
G = first_community(G)
G = second_community(G)
G.add_edge(5, 15)
nx.draw(G, with_labels=1)
plt.show()
nx.write_gml(G, "community.gml")
输出:
![]()
对集群的影响
4。群集上的级联。
Python 3
import networkx as nx
import matplotlib.pyplot as plt
def set_all_B(G):
for i in G.nodes():
G.nodes[i]['action'] = 'B'
return G
def set_A(G, list1):
for i in list1:
G.nodes[i]['action'] = 'A'
return G
def get_colors(G):
color = []
for i in G.nodes():
if (G.nodes[i]['action'] == 'B'):
color.append('red')
else:
color.append('green')
return color
def recalculate(G):
dict1 = {}
a = 3
b = 2
for i in G.nodes():
neigh = G.neighbors(i)
count_A = 0
count_B = 0
for j in neigh:
if (G.nodes[j]['action'] == 'A'):
count_A += 1
else:
count_B += 1
payoff_A = a * count_A
payoff_B = b * count_B
if (payoff_A >= payoff_B):
dict1[i] = 'A'
else:
dict1[i] = 'B'
return dict1
def reset_node_attributes(G, action_dict):
for i in action_dict:
G.nodes[i]['action'] = action_dict[i]
return G
def Calculate(G):
terminate = True
count = 0
c = 0
while (terminate and count < 100):
count += 1
# action_dict will hold a dictionary
action_dict = recalculate(G)
G = reset_node_attributes(G, action_dict)
colors = get_colors(G)
if (colors.count('red') == len(colors) or colors.count('green') == len(colors)):
terminate = False
if (colors.count('green') == len(colors)):
c = 1
if (c == 1):
print('cascade complete')
else:
print('cascade incomplete')
nx.draw(G, with_labels=1, node_color=colors, node_size=800)
plt.show()
G = nx.Graph()
G.add_nodes_from(range(13))
G.add_edges_from(
[(0, 1), (0, 6), (1, 2), (1, 8), (1, 12),
(2, 9), (2, 12), (3, 4), (3, 9), (3, 12),
(4, 5), (4, 12), (5, 6), (5, 10), (6, 8),
(7, 8), (7, 9), (7, 10), (7, 11), (8, 9),
(8, 10), (8, 11), (9, 10), (9, 11), (10, 11)])
list2 = [[0, 1, 2, 3], [0, 2, 3, 4], [1, 2, 3, 4],
[2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 12],
[2, 3, 4, 12], [0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5, 6, 12]]
for list1 in list2:
print(list1)
G = set_all_B(G)
G = set_A(G, list1)
colors = get_colors(G)
nx.draw(G, with_labels=1, node_color=colors, node_size=800)
plt.show()
Calculate(G)
输出:
[0, 1, 2, 3]
cascade incomplete
[0, 2, 3, 4]
cascade incomplete
[1, 2, 3, 4]
cascade incomplete
[2, 3, 4, 5]
cascade incomplete
[3, 4, 5, 6]
cascade incomplete
[4, 5, 6, 12]
cascade incomplete
[2, 3, 4, 12]
cascade incomplete
[0, 1, 2, 3, 4, 5]
cascade incomplete
[0, 1, 2, 3, 4, 5, 6, 12]
cascade complete
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