现在有一个无向有权网络的csv文件,第一列为源节点id,第二列为目标节点id,第三列为边的权重,其中节点命名方式不是整数序列,注意节点个数获取的计算方式。定义该网络中节点的度为与节点相连接的边的权重之和。求该网络的聚集系数。请你仔细思考检查后,输出完整的代码,要求不使用外部函数
时间: 2024-05-01 18:21:50 浏览: 19
好的,我会根据您的要求编写代码。首先,我们需要读取csv文件并将其转换为邻接矩阵。代码如下:
```python
# 读取csv文件
with open('network.csv') as f:
data = f.readlines()
# 去除首行的表头
data = data[1:]
# 获取节点名称列表
nodes = []
for line in data:
source, target, weight = line.strip().split(',')
if source not in nodes:
nodes.append(source)
if target not in nodes:
nodes.append(target)
# 创建邻接矩阵
n = len(nodes)
adj_matrix = [[0] * n for _ in range(n)]
for line in data:
source, target, weight = line.strip().split(',')
i = nodes.index(source)
j = nodes.index(target)
weight = int(weight)
adj_matrix[i][j] = weight
adj_matrix[j][i] = weight # 无向图需要将邻接矩阵对称
```
接下来,我们需要计算每个节点的聚集系数。聚集系数是指节点的邻居之间实际存在的边数占所有可能存在的边数的比例。计算聚集系数的代码如下:
```python
# 计算每个节点的聚集系数
clustering_coeffs = []
for i in range(n):
neighbors = [j for j in range(n) if adj_matrix[i][j] > 0]
num_edges = len(neighbors) * (len(neighbors) - 1) // 2 # 可能的边数
if num_edges == 0:
clustering_coeffs.append(0)
else:
num_existing_edges = 0
for j in range(len(neighbors)):
for k in range(j+1, len(neighbors)):
if adj_matrix[neighbors[j]][neighbors[k]] > 0:
num_existing_edges += 1
clustering_coeffs.append(num_existing_edges / num_edges)
```
最后,我们需要计算整个网络的聚集系数。网络的聚集系数是所有节点的聚集系数的平均值。计算网络聚集系数的代码如下:
```python
# 计算整个网络的聚集系数
total_clustering_coeff = sum(clustering_coeffs) / n
print('聚集系数:', total_clustering_coeff)
```
综上所述,完整的代码如下:
```python
# 读取csv文件
with open('network.csv') as f:
data = f.readlines()
# 去除首行的表头
data = data[1:]
# 获取节点名称列表
nodes = []
for line in data:
source, target, weight = line.strip().split(',')
if source not in nodes:
nodes.append(source)
if target not in nodes:
nodes.append(target)
# 创建邻接矩阵
n = len(nodes)
adj_matrix = [[0] * n for _ in range(n)]
for line in data:
source, target, weight = line.strip().split(',')
i = nodes.index(source)
j = nodes.index(target)
weight = int(weight)
adj_matrix[i][j] = weight
adj_matrix[j][i] = weight # 无向图需要将邻接矩阵对称
# 计算每个节点的聚集系数
clustering_coeffs = []
for i in range(n):
neighbors = [j for j in range(n) if adj_matrix[i][j] > 0]
num_edges = len(neighbors) * (len(neighbors) - 1) // 2 # 可能的边数
if num_edges == 0:
clustering_coeffs.append(0)
else:
num_existing_edges = 0
for j in range(len(neighbors)):
for k in range(j+1, len(neighbors)):
if adj_matrix[neighbors[j]][neighbors[k]] > 0:
num_existing_edges += 1
clustering_coeffs.append(num_existing_edges / num_edges)
# 计算整个网络的聚集系数
total_clustering_coeff = sum(clustering_coeffs) / n
print('聚集系数:', total_clustering_coeff)
```