图分类

以下是一个使用Python编程语言进行图分类的示例代码：

import networkx as nx
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 构建图数据
G1 = nx.Graph()
G1.add_edges_from([(1, 2), (2, 3), (3, 4)])

G2 = nx.Graph()
G2.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5)])

G3 = nx.Graph()
G3.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)])

G4 = nx.Graph()
G4.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)])

# 将图数据转换为特征向量
def graph_to_features(graph):
    features = []
    for node in graph.nodes():
        degree = nx.degree(graph, node)
        features.append(degree)
    return features

X = []
y = []

# 初始化特征向量和标签
X.append(graph_to_features(G1))
y.append(0)

X.append(graph_to_features(G2))
y.append(0)

X.append(graph_to_features(G3))
y.append(1)

X.append(graph_to_features(G4))
y.append(1)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用支持向量机进行分类
clf = svm.SVC()
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

上述代码中，首先构建了四个图数据G1、G2、G3和G4，每个图表示不同的类别。然后通过graph_to_features函数将图数据转换为特征向量X，并为每个图数据标记类别标签y。接下来，使用train_test_split函数将数据划分为训练集和测试集。最后，使用支持向量机（SVM）算法进行分类，并计算准确率。