Dear Kushal,

I suggest you to see links and attached files in topics.

- Diffusion Through Multilayer Networks - Diva Portal

uu.diva-portal.org/smash/get/diva2:805149/FULLTEXT01.pdf

- Detecting the Influence of Spreading in Social Networks with Excitable ...

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423969/

Best regards

import networkx as nx

import kushal_lib as klib

import random

import scipy.stats as stats

from collections import Counter

import numpy as np

import matplotlib.pyplot as plt

import statistics as st

#g=nx.read_edgelist("zebra_edge_list", nodetype=int); beta=0.10

g=nx.read_adjlist("SIR_Network_test", nodetype=int); beta=0.35

r=nx.degree_assortativity_coefficient(g)

random.seed(2018)

N=500

suceptible=[]

infected=[]

recovered=[]

candidates=[]

r_list=[]

candidates_hist=dict()

sigma=dict()

sigma1=dict()

for node in g.nodes():

gc=0.0; r_list=[]

for iters in range(0, N):

infected=[node]; suceptible=g.nodes(); suceptible.remove(node)

candidates_hist=dict()

candidates=[]; recovered=[]; lc=0.0

while len(infected)!=0:

size=len(suceptible+infected+recovered)

if size!=g.number_of_nodes():

print "issue"

break

for i in infected:

for nbr in nx.neighbors(g, i):

if nbr in suceptible and nbr not in infected:

candidates.append(nbr)

recovered.extend(infected)

#print candidates

infected=[]

candidates_hist=dict(Counter(candidates))

candidates=list(set(candidates))

if len(candidates)==0:

break

suceptible=list(set(suceptible)-set(candidates))

size=len(suceptible+infected+recovered+candidates)

if size!=g.number_of_nodes():

print "issue"

break

#print suceptible

candidates=[]

for c, f in candidates_hist.items():

indicator=0

for cntr in range(0, f):

if random.uniform(0, 1)