Minimum cost node cut

Question

I am interested in solving the following problem:

Given an undirected graph whose vertices are weighted, find a subset of vertices of minimal weight whose removal disconnects the graph.

Is there any known algorithm solving this problem?

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Here is an attempt at a solution (in python), using network flow:

deg_G = nx.degree(G)
max_weight = max([deg for i,deg in deg_G])+1

st_Weighted_Complement_G = nx.DiGraph()
r = np.arange(len(Complement_G.nodes))

nodes = ['s','t']
edges = []
for i in r:
    nIn = (str(i)+'in')
    nOut = (str(i)+'out')
    nodes.extend([nIn,nOut])
    edges.extend([(nIn,nOut,{'capacity':deg_G[i],'weight':deg_G[i]}),('s',nIn,{'capacity':math.inf,'weight':0}),
                  (nOut,'t',{'capacity':math.inf,'weight':0})])

for edge in Complement_G.edges:
    print(edge[0],edge[1])
    edges.extend([((str(edge[0]))+'out',(str(edge[1]))+'in',{'capacity':max_weight,'weight':0}),
                  ((str(edge[1]))+'out',(str(edge[0]))+'in',{'capacity':max_weight,'weight':0})])

print(edges)
st_Weighted_Complement_G.add_nodes_from(nodes)
st_Weighted_Complement_G.add_weighted_edges_from(edges)

mincostFlow = nx.max_flow_min_cost(st_Weighted_Complement_G, 's', 't',capacity='capacity',weight='weight')
print(mincostFlow)