在MultiGraph中聚合边缘属性

import networkx as nx
import pandas as pd

# 创建一个MultiGraph
G = nx.MultiGraph()
G.add_edge(1, 2, weight=3, attr1='a')
G.add_edge(1, 2, weight=4, attr1='b')
G.add_edge(2, 3, weight=5, attr1='c')

# 将MultiGraph转换为DataFrame
edges_df = nx.to_pandas_edgelist(G)

# 使用groupby和agg函数聚合边缘属性
aggregated_edges_df = edges_df.groupby(['source', 'target']).agg(
    weight=('weight', 'sum'),
    attr1=('attr1', lambda x: ','.join(x))
).reset_index()

print(aggregated_edges_df)

   source  target  weight attr1
0       1       2       7    a,b
1       2       3       5      c

import networkx as nx

# 创建一个MultiGraph
G = nx.MultiGraph()
G.add_edge(1, 2, weight=3, attr1='a')
G.add_edge(1, 2, weight=4, attr1='b')
G.add_edge(2, 3, weight=5, attr1='c')

# 获取边缘属性
weight_attr = nx.get_edge_attributes(G, 'weight')
attr1_attr = nx.get_edge_attributes(G, 'attr1')

# 自定义聚合函数
def aggregate_weight(edges):
    return sum(edges)

def aggregate_attr1(edges):
    return ','.join(edges)

# 聚合边缘属性
aggregated_edges = {}
for (u, v), data in G.edges(data=True, keys=True):
    key = (u, v)
    if key not in aggregated_edges:
        aggregated_edges[key] = {'weight': [], 'attr1': []}
    aggregated_edges[key]['weight'].append(data['weight'])
    aggregated_edges[key]['attr1'].append(data['attr1'])

for key in aggregated_edges:
    aggregated_edges[key]['weight'] = aggregate_weight(aggregated_edges[key]['weight'])
    aggregated_edges[key]['attr1'] = aggregate_attr1(aggregated_edges[key]['attr1'])

print(aggregated_edges)

{(1, 2): {'weight': 7, 'attr1': 'a,b'}, (2, 3): {'weight': 5, 'attr1': 'c'}}