import networkx as nx # 构造一个弦图 G = nx.Graph() G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]) # 判断是否为弦图 print(nx.is_chordal(G)) # 输出 True # 获取最大团 cliques = nx.chordal_graph_cliques(G) max_clique = max(cliques, key=len) print(max_clique) # 输出 [3, 4, 5, 6] # 计算树宽 treewidth = nx.chordal_graph_treewidth(G) print(treewidth) # 输出 2
时间: 2023-09-16 15:08:56 浏览: 55
你的代码有一些错误,因为运行时会出现一个错误,即找不到 `chordal_graph_cliques` 函数。我想这可能是因为你使用的是较旧的 NetworkX 版本。以下是一个更新的代码示例,可以在较新的 NetworkX 版本中运行:
```python
import networkx as nx
# 构造一个弦图
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)])
# 判断是否为弦图
print(nx.is_chordal(G)) # 输出 True
# 获取最大团
cliques = list(nx.chordal_graph_cliques(G))
max_clique = max(cliques, key=len)
print(max_clique) # 输出 [3, 4, 5, 6]
# 计算树宽
treewidth = nx.chordal_graph_treewidth(G)
print(treewidth) # 输出 2
```
在上述代码中,我们使用 `list` 函数将 `chordal_graph_cliques` 函数的迭代器转换为列表。此外,我们还可以使用 `nx.find_cliques` 函数来获取弦图的所有团,但是这个函数不会保证团的最大性质。
相关问题
import networkx as nxfrom sklearn.model_selection import train_test_splitfrom networkx.algorithms.link_prediction import katz_similarityfrom sklearn.metrics import roc_auc_score, average_precision_score# 加载数据集edges = []with open("email-Eu-core.txt") as f: for line in f: if line.startswith("#"): continue edge = line.strip().split() edges.append((int(edge[0]), int(edge[1])))# 划分训练集和测试集train_edges, test_edges = train_test_split(edges, test_size=0.1, random_state=42)# 构建无向图graph = nx.Graph()graph.add_edges_from(train_edges)# 计算相似度katz_scores = katz_similarity(graph, max_l=200, beta=0.01)# 计算AUC值y_true = [1 if edge in test_edges else 0 for edge in graph.edges()]y_scores = [katz_scores[edge] for edge in graph.edges()]auc = roc_auc_score(y_true, y_scores)# 计算Precisionk = 100top_k_edges = sorted(graph.edges(), key=lambda x: katz_scores[x], reverse=True)[:k]y_true = [1 if edge in test_edges else 0 for edge in top_k_edges]y_scores = [katz_scores[edge] for edge in top_k_edges]precision = average_precision_score(y_true, y_scores)print("Katz Algorithm")print("AUC: {:.4f}".format(auc))print("Precision@{}: {:.4f}".format(k, precision))有什么问题
这段代码中有一个语法错误,即第一行的 import 语句中没有正确地使用换行符。应该在 `import networkx as nx` 后添加一个换行符。
另外,代码中的 katz_similarity() 函数调用中使用了一个 max_l 参数,这个参数是不必要的,可以直接去掉,因为该函数不支持传递该参数。
最后,代码未定义变量 k,所以在计算 Precision 时会出错。您应该在代码开头定义一个 k 变量,或者将 k 的值硬编码到代码中。
下面是修改后的代码:
```python
import networkx as nx
from sklearn.model_selection import train_test_split
from networkx.algorithms.link_prediction import katz_similarity
from sklearn.metrics import roc_auc_score, average_precision_score
# 加载数据集
edges = []
with open("email-Eu-core.txt") as f:
for line in f:
if line.startswith("#"):
continue
edge = line.strip().split()
edges.append((int(edge[0]), int(edge[1])))
# 划分训练集和测试集
train_edges, test_edges = train_test_split(edges, test_size=0.1, random_state=42)
# 构建无向图
graph = nx.Graph()
graph.add_edges_from(train_edges)
# 计算相似度
katz_scores = katz_similarity(graph, beta=0.01)
# 计算AUC值
y_true = [1 if edge in test_edges else 0 for edge in graph.edges()]
y_scores = [katz_scores[edge] for edge in graph.edges()]
auc = roc_auc_score(y_true, y_scores)
# 计算Precision
k = 100
top_k_edges = sorted(graph.edges(), key=lambda x: katz_scores[x], reverse=True)[:k]
y_true = [1 if edge in test_edges else 0 for edge in top_k_edges]
y_scores = [katz_scores[edge] for edge in top_k_edges]
precision = average_precision_score(y_true, y_scores)
print("Katz Algorithm")
print("AUC: {:.4f}".format(auc))
print("Precision@{}: {:.4f}".format(k, precision))
```
请注意,这段代码依赖于 `email-Eu-core.txt` 数据集文件,您需要确保该文件存在并且与代码在同一目录下。
G.add_edges_from(graph) 构建图后用字典形式展示
你可以使用 `nx.to_dict_of_dicts(G)` 方法将 NetworkX 图对象 G 转换成一个嵌套字典的形式,其中外层字典的键是每个节点的标识符,对应的值是一个字典,表示该节点与其它节点之间的边。具体实现代码如下:
```python
import networkx as nx
# 构建一个图对象 G
graph = [(1, 2), (2, 3), (3, 4)]
G = nx.Graph()
G.add_edges_from(graph)
# 将图对象 G 转换成嵌套字典的形式
graph_dict = nx.to_dict_of_dicts(G)
print(graph_dict)
```
输出结果为:
```
{1: {2: {}}, 2: {1: {}, 3: {}}, 3: {2: {}, 4: {}}, 4: {3: {}}}
```
其中 `{2: {}}` 表示节点 1 与节点 2 之间有一条无权无向边,`{1: {}, 3: {}}` 表示节点 2 与节点 1、3 之间各有一条无权无向边,以此类推。