NetworkX中提取带高权重粗边的子图问题求助

你的问题根源是整个原图本身是强连通的，所以strongly_connected_components会返回所有节点。要提取高权重边的子图，得先过滤边再处理，具体步骤如下：

1. 筛选高权重边 #

从计数结果ct里挑出出现次数（即边权重）大于2的边：

# 过滤权重>2的边，阈值可根据需求调整
high_weight_edges = [edge for edge, cnt in ct.items() if cnt > 2]

2. 构建高权重边子图 #

两种方式生成子图：

# 方式一：从已有图中提取边子图
high_weight_subgraph = pattern_g.edge_subgraph(high_weight_edges)

# 方式二：直接新建图并添加带权重的高权重边
high_weight_subgraph = nx.DiGraph()
for edge, cnt in ct.items():
    if cnt > 2:
        high_weight_subgraph.add_edge(*edge, weight=cnt)

3. 绘制高权重子图 #

计算边宽并绘制：

widths = [ct[edge] for edge in high_weight_subgraph.edges]
nx.draw(high_weight_subgraph, node_color='orange', with_labels=True, width=widths)
plt.show()

可选：提取子图中的强连通分量 #

如果需要在高权重子图里找强连通分量，直接对子图调用方法即可：

# 获取所有强连通分量
scc_list = list(nx.strongly_connected_components(high_weight_subgraph))
# 取最大的强连通分量绘制
largest_scc = max(scc_list, key=len)
scc_subgraph = high_weight_subgraph.subgraph(largest_scc)

# 绘制强连通分量子图
scc_widths = [ct[edge] for edge in scc_subgraph.edges]
nx.draw(scc_subgraph, node_color='orange', with_labels=True, width=scc_widths)
plt.show()

内容的提问来源于stack exchange，提问作者black street boy