如何按日计算NetworkX图中指定节点的度中心性并优化代码

我有一个涵盖数月事件的NetworkX图，想要查看特定节点的中心性分数随时间的变化。计划用多种中心性指标，所以写了个函数来筛选特定发送者和日期，构建NetworkX图并计算度中心性，结果存入DataFrame。但现在代码比较繁琐，而且输出里包含35、18这些无关节点，我只需要节点A的结果，想问问有没有更优的实现方式。

附上测试代码、当前输出及期望输出：

测试代码 #

import numpy as np
import pandas as pd
from datetime import datetime
import networkx as nx

df = pd.DataFrame({'feature':['A','B','A','B','A','B','A','B','A','B'],
                   'feature2':['18','78','35','14','57','68','57','17','18','78'],
                   'timestamp':['2017-01-20T11','2017-01-01T13',
                           '2017-01-02T12','2017-02-01T13',
                           '2017-03-01T14','2017-05-01T15',
                           '2017-04-01T16','2017-04-01T17',
                          '2017-12-01T17','2017-12-01T19']})
df['timestamp'] = pd.to_datetime(pd.Series(df['timestamp']))
df['date'], df['time']= df.timestamp.dt.date,  df.timestamp.dt.time

def test(feature,date,name,col_name,nx_measure):
    feature = df[df['feature']== feature]
    feature['date_str'] = feature['date'].astype(str)
    one_day = feature[feature['date_str']==date]
    oneDay_graph =nx.from_pandas_edgelist(one_day, source = 'feature', target = 'feature2', create_using=nx.DiGraph)
    name = pd.DataFrame()
    name['feature']= nx_measure(oneDay_graph).keys()  
    name[col_name]= nx_measure(oneDay_graph).values()
    name['date'] = date
    return name

a =test('A','2017-01-02','degree','degree',nx.degree_centrality)
b = test('A','2017-01-20','degree','degree',nx.degree_centrality)

a.append(b)

当前输出 #

feature degree  date
0   A   1.0 2017-01-02
1   35  1.0 2017-01-02
0   A   1.0 2017-01-20
1   18  1.0 2017-01-20

期望输出 #

feature degree  date
0   A   1.0 2017-01-02
0   A   1.0 2017-01-20

可以从简化逻辑、精准过滤、批量处理三个角度优化代码，直接得到目标节点的结果：

改进后的代码 #

import numpy as np
import pandas as pd
import networkx as nx

df = pd.DataFrame({'feature':['A','B','A','B','A','B','A','B','A','B'],
                   'feature2':['18','78','35','14','57','68','57','17','18','78'],
                   'timestamp':['2017-01-20T11','2017-01-01T13',
                           '2017-01-02T12','2017-02-01T13',
                           '2017-03-01T14','2017-05-01T15',
                           '2017-04-01T16','2017-04-01T17',
                          '2017-12-01T17','2017-12-01T19']})
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['date_str'] = df['timestamp'].dt.date.astype(str)  # 提前统一处理日期格式，避免重复转换

def get_node_centrality(target_node, dates, col_name, nx_measure):
    # 提前筛选目标节点的所有数据，减少重复过滤
    target_data = df[df['feature'] == target_node]
    result_rows = []
    
    for date in dates:
        daily_data = target_data[target_data['date_str'] == date]
        if daily_data.empty:
            continue  # 无数据时直接跳过
        
        # 构建当日图并计算中心性（只算一次，避免重复调用）
        daily_graph = nx.from_pandas_edgelist(daily_data, source='feature', target='feature2', create_using=nx.DiGraph)
        centrality = nx_measure(daily_graph)
        
        # 只提取目标节点的结果，组装成字典
        result_rows.append({
            'feature': target_node,
            col_name: centrality.get(target_node, 0.0),  # 节点不存在时返回0.0作为默认值
            'date': date
        })
    
    return pd.DataFrame(result_rows)

# 调用函数，传入目标节点和需要查询的日期列表
final_result = get_node_centrality('A', ['2017-01-02', '2017-01-20'], 'degree', nx.degree_centrality)
print(final_result)

优化点说明 #

1. 减少重复计算：原函数两次调用nx_measure，现在只计算一次中心性分数，提升效率。
2. 精准过滤结果：直接提取目标节点的分数，无需生成包含所有节点的DataFrame，彻底避免无关节点出现。
3. 批量处理日期：支持传入日期列表，一次性处理多个日期，不用多次调用函数再拼接结果。
4. 增加容错处理：判断当日是否有数据，避免空数据导致的报错。

运行后输出与期望完全一致：

feature  degree        date
0       A     1.0  2017-01-02
1       A     1.0  2017-01-20

内容的提问来源于stack exchange，提问作者Rebecca James