playground.py

import networkx as nx
from graphGeneration import Cora, CiteSeer, PubMed, connSW, ER
from time import time
from IM import greedyLT, greedyIC, eigen, degree, pi, sigma, Netshield, celf, celfpp
from score import scoreIC, Y, SobolT, sobols, IE
from simulation import simulationIC, simulationLT
import statistics as s
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
import torch_geometric.datasets as ds
import random



# Create a small directed graph
g = nx.DiGraph()
g.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6)])

# Set edge weights
edge_thresholds = {
    (0, 1): 0.5,
    (0, 2): 0.5,
    (1, 3): 0.5,
    (2, 3): 0.5,
    (3, 4): 0.5,
    (4, 5): 0.5,
    (4, 6): 0.5,
}

config = mc.Configuration()

for a, b in g.edges():
    weight = random.randrange(40,80)
    weight = round(weight / 100, 2)
    config.add_edge_configuration("threshold", (a, b), weight)
    g[a][b]['weight'] = weight

budget = 2

# Run the greedy algorithm with the LT function
selected_nodes = greedyLT(g, config, budget)
print("LT Selected nodes:", selected_nodes)

# Run the greedy algorithm with the IC function
selected_nodes = greedyIC(g, config, budget)
print("IC Selected nodes:", selected_nodes)

# Run the CELF algorithm with the IC function
selected_nodes = celf(g, config, budget)
print("CELF IC Selected nodes:", selected_nodes)

# Run the CELFPP algorithm with the IC function
selected_nodes = celfpp(g, config, budget)
print("CELFPP IC Selected nodes:", selected_nodes)