I have been trying to study the impact on a network by looking at deletions of different combinations of nodes.
To study this I have used the networkx graph theory metric, global efficiency. But, I figured that the networkx code ignores weight when calculating global efficiency. So, I went in and changed the source code and added weight as a metric. It seems to be working and is giving me different values than the non-weighted approach but is exceptionally slow (about 20 times).
How can I speed up these computations?
##The code I am running
import networkx
import numpy as np
from networkx import algorithms
from networkx.algorithms import efficiency
from networkx.algorithms.efficiency import global_efficiency
import pandas
data=pandas.read_csv("ones.csv")
lol = data.values.tolist()
data=pandas.read_csv("twos.csv")
lol2 = data.values.tolist()
combo=[["10pp", "10d"]]
GE_list=[]
for row in combo:
values = row
datasafe=pandas.read_csv("b1.csv", index_col=0)
datasafe.loc[values, :] = 0
datasafe[values] = 0
g=networkx.from_pandas_adjacency(datasafe)
ge=global_efficiency(g)
GE_list.append(ge)
extra=[""]
extra2=["full"]
combo.append(extra)
combo.append(extra2)
datasafe=pandas.read_csv("b1.csv", index_col=0)
g=networkx.from_pandas_adjacency(datasafe)
ge=global_efficiency(g)
GE_list.append(ge)
values = ["s6-8","p9-46v","p47r","p10p","IFSp","IFSa",'IFJp','IFJa','i6-8','a9-46v','a47r','a10p','9p','9a','9-46d','8C','8BL','8AV','8AD','47s','47L','10pp','10d','46','45','44']
datasafe=pandas.read_csv("b1.csv", index_col=0)
datasafe.loc[values, :] = 0
datasafe[values] = 0
g=networkx.from_pandas_adjacency(datasafe)
ge=global_efficiency(g)
GE_list.append(ge)
output=pandas.DataFrame(list(zip(combo, GE_list)))
output.to_csv('delete 1.csv',index=None)
##The change I made to the original networkx code
try:
eff = 1 / nx.shortest_path_length(G, u, v)
## changed to
try:
eff = 1 / nx.shortest_path_length(G, u, v, weight='weight')
Previously with my unweighted graphs I was able to process my data in 2 hours, currently its taking the same time to do a twentieth of the data. Please do suggest any improvements to my code or any other pieces of code that I can run.
Ps-I don't have a great understanding of python, so please do bear with me :)
