使用Python改进从有向图投影的无向图的创建

Question

I have a (bipartite) directed graph where a legal entity is connected by an edge to each candidate it sponsored or cosponsored. From it, I want a second (unipartite), undirected one, G , projected from the first in which nodes are candidates and the weighted edges connecting them indicate how many times they received money together from the same legal entity.

All information are encoded in a dataframe candidate_donator where each candidate are associated to a tuple containing who donated to him.

I'm using Networkx to create the network and want optimize my implementation because it is taking very long. My original approach is:

candidate_donator = df.groupby('candidate').agg({'donator': lambda x: tuple(set(x))})

import itertools
candidate_pairs= list(itertools.combinations(candidate_donator .index, 2)) #creating all possible unique combinations of pair candidates ~83 M

for cpf1, cpf2 in candidate_pairs:
    donators_filter = list(filter(set(candidate_pairs.loc[cpf1]).__contains__, candidate_pairs.loc[cpf2]))
    G.add_edge(cpf1, cpf2, weight = len(donators_filter ))      

Answer 1

Try this:

#list of donators per candidate
candidate_donator = df.groupby('candidate').agg({'donator': lambda x: tuple(set(x))})
#list of candidates per donator
donator_candidate = df.groupby('donator').agg({'candidate': lambda x: tuple(set(x))})

#for each candidate
for candidate_idx in candidate_donator.index:
    #for each donator connected to this candidate
    for donator_list in candidate_donator.loc[candidate_idx, 'donator']:
        for last_candidate in donator_list:
            #existing edge, add weight
            if G.has_edge(candidate_idx, last_candidate):
                G[candidate_idx][last_candidate] += 0.5
            #non existing edge, weight = 0.5 (every edge will be added twice)
            else:
                G.add_edge(candidate_idx, last_candidate, weight = 0.5)