将一个有方向的加权网络图（Source,target,weight）转化为无方向的加权网络图，且新的边的权重为原来有向图中两个方向的和

将有向加权图转为无向加权图的过程称为图的“邻接矩阵对称化”或“无向化”。在这个过程中，对于每一对源节点`source`和目标节点`target`，如果原始图中有从`source`到`target`的边，权重设为`weight`；同时，因为是无向图，还需要加上从`target`到`source`的边，权重则是从`source`到`target`边的权重值。

下面是一个简单的例子用Python来实现这个过程：

def directed_to_undirected(weighted_graph, weight_key='weight'):
    undirected_weighted_graph = {}
    
    for source, edges in weighted_graph.items():
        for target, weight in edges.items():
            if target not in undirected_weighted_graph:
                undirected_weighted_graph[target] = {source: weight}
            else:
                undirected_weighted_graph[target][source] = weight if source not in undirected_weighted_graph[target] else undirected_weighted_graph[target][source] + weight

            # 对称添加边，权重为两者之和
            if source != target and (source, target) not in undirected_weighted_graph:
                undirected_weighted_graph[source][target] = weight
            elif source != target and (source, target) in undirected_weighted_graph:
                undirected_weighted_graph[source][target] += weight
    
    return undirected_weighted_graph

# 示例：
weighted_directed_graph = {
    'A': {'B': 5, 'C': 7},
    'B': {'D': 3},
    'C': {},
    'D': {'E': 4}
}

undirected_weighted_graph = directed_to_undirected(weighted_directed_graph)

这里的`weight_key`参数可以根据实际数据结构中的权重键进行修改，默认为`'weight'`。结果`undirected_weighted_graph`就是无向图的形式，其中新边的权重是原边的双向权重总和。