均衡加权图

如何解决均衡加权图

是否有一种算法可以改变多向图中的某个方向，使其权重均匀分布？我知道我们可以通过删除或添加边来改变多向图中的方向，但我不知道我应该改变哪条边

G = ox.graph_from_bBox(north,south,east,west,network_type = 'drive',simplify=True)

for u,v,data in G.edges(data=True):
  data['weight'] = Somecomputation()

ev = [data['weight'] for _,_,data in G.edges(data=True)]

r = plt.Boxplot(ev)

结果如下 boxplot。但是异常值太多了，至少我希望通过在我的图中添加或删除边来使分布没有那么多异常值。

这就是我添加或删除边缘的方式

nodes,edges = ox.graph_to_gdfs(G)

oneway = edges[edges.oneway == True]
twoway = edges[edges.oneway == False]

def add_edge(gdf,u,v):
  gdf_copy = gdf
  index = pd.MultiIndex.from_tuples([(u,0)],names=["u","v","key"])
  query = gdf_copy.query("u == "+str(v)+" and v == "+str(u))
  df = pd.DataFrame(data=query.values,index=index)
  gdf_copy = gdf_copy.append(df)

  return gdf_copy

def remove_edge(gdf,v):
  gdf_copy = gdf
  gdf_copy = gdf_copy[~gdf_copy.isin(gdf_copy.query("u == "+str(u)+" and v == "+str(v)))]
  gdf_copy = gdf_copy[gdf_copy.length.notna()]

  return gdf_copy

list_edge2add = [...] #list of tuple
list_edge2remove = [...] #list of tuple

# applying add_edge() on oneway and remove_edge() on twoway by for looping

gdf = oneway.append(twoway)

# I get new graph
G = ox.graph_from_gdfs(nodes,gdf)

for u,data in G.edges(data=True)]

r = plt.Boxplot(ev)

问题是我不知道我应该使用什么 list_edge2add 和 list_edge2remove 以获得更均匀的分布