import networkx as nx from neo4j import GraphDatabase from torch_geometric.data import Data # Connect to Neo4j database driver = GraphDatabase.driver(uri, auth=(username, password)) # Define a Cypher query to retrieve nodes and relationships from Neo4j query = """ MATCH (n)-[r]->(m) RETURN id(n) AS source, id(m) AS target, type(r) AS edge_type, labels(n) AS source_labels, labels(m) AS target_labels, properties(n) AS source_props, properties(m) AS target_props """ # Execute the query and retrieve the results with driver.session() as session: results = session.run(query) # Convert the query results to a NetworkX graph graph = nx.MultiDiGraph() for record in results: graph.add_edge(record['source'], record['target'], key=record['edge_type'], source_labels=record['source_labels'], target_labels=record['target_labels'], source_props=record['source_props'], target_props=record['target_props']) # Convert the NetworkX graph to a PyTorch Geometric Data object x = [] edge_index = [] edge_attr = [] for node in graph.nodes(): node_attrs = [] for label in graph.nodes[node]['labels']: node_attrs.append(label) for prop in graph.nodes[node]['source_props']: node_attrs.append(prop) x.append(node_attrs) for source, target, data in graph.edges(keys=True, data=True): edge_index.append([source, target]) edge_attrs = [] for label in data['source_labels']: edge_attrs.append(label) for prop in data['properties']: edge_attrs.append(prop) edge_attr.append(edge_attrs) data = Data(x=torch.tensor(x), edge_index=torch.tensor(edge_index).t().contiguous(), edge_attr=torch.tensor(edge_attr))详细注释

这段代码的作用是从一个Neo4j数据库中检索节点和关系，将它们转化为一个PyTorch Geometric Data对象。以下是代码的详细注释：

# 导入所需的包
import networkx as nx
from neo4j import GraphDatabase
from torch_geometric.data import Data

# 连接到Neo4j数据库
driver = GraphDatabase.driver(uri, auth=(username, password))

# 定义一个Cypher查询语句，用于从Neo4j数据库中检索节点和关系。
query = """
MATCH (n)-[r]->(m)
RETURN id(n) AS source, id(m) AS target, type(r) AS edge_type,
       labels(n) AS source_labels, labels(m) AS target_labels,
       properties(n) AS source_props, properties(m) AS target_props
"""

# 执行查询并检索结果
with driver.session() as session:
    results = session.run(query)

# 将查询结果转化为一个NetworkX图
graph = nx.MultiDiGraph()
for record in results:
    graph.add_edge(record['source'], record['target'], key=record['edge_type'],
                   source_labels=record['source_labels'], target_labels=record['target_labels'],
                   source_props=record['source_props'], target_props=record['target_props'])

# 将NetworkX图转化为一个PyTorch Geometric Data对象
x = []
edge_index = []
edge_attr = []

# 遍历图中的每个节点，将节点的属性存储在x列表中
for node in graph.nodes():
    node_attrs = []
    for label in graph.nodes[node]['labels']:
        node_attrs.append(label)
    for prop in graph.nodes[node]['source_props']:
        node_attrs.append(prop)
    x.append(node_attrs)

# 遍历图中的每条边，将边的属性存储在edge_index和edge_attr列表中
for source, target, data in graph.edges(keys=True, data=True):
    edge_index.append([source, target])
    edge_attrs = []
    for label in data['source_labels']:
        edge_attrs.append(label)
    for prop in data['properties']:
        edge_attrs.append(prop)
    edge_attr.append(edge_attrs)

# 创建一个PyTorch Geometric Data对象
data = Data(x=torch.tensor(x), edge_index=torch.tensor(edge_index).t().contiguous(),
            edge_attr=torch.tensor(edge_attr))

这段代码的主要步骤包括：

1. 首先，连接到一个Neo4j数据库。
2. 然后，定义一个Cypher查询语句来检索节点和关系。
3. 执行查询，并将结果转化为一个NetworkX图。
4. 遍历图中的每个节点，将节点的属性存储在x列表中。
5. 遍历图中的每条边，将边的属性存储在edge_index和edge_attr列表中。
6. 最后，创建一个PyTorch Geometric Data对象，其中包含节点属性x、边属性edge_attr和边索引edge_index。

这段代码的目的是将一个Neo4j图转化为一个PyTorch Geometric Data对象，以便进行图神经网络的训练和推理。