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profile_undirected_hp_er.py
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profile_undirected_hp_er.py
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# from igraph import *
import igraph as ig
import easygraph as eg
from easygraph import multi_source_dijkstra
from easygraph.functions.graph_generator import erdos_renyi_M
from benchmark import benchmark
import sys
import random
import os
import numpy as np
n = 5
def random_nodes(nodes_num, start_idx, end_idx, seed=0):
random.seed(seed)
node_list = []
for i in range(nodes_num):
node_list.append(random.randint(start_idx, end_idx))
return node_list
def pre_data(path, file):
# print(file)
data = np.loadtxt(path+file,usecols=(0,1))
np.savetxt('directed_datasets/new_data_'+file, data, fmt='%i')
return 'directed_datasets/new_data_'+file
if __name__ == "__main__":
print('for directed networks..............')
n_sizelist = [10000, 50000, 100000, 200000]
for size in n_sizelist:
m = size * 2
# =======================EasyGraph=======================
benchmark('erdos_renyi_M(size, edge=m, directed=False)', globals=globals(), n=n)
print(f"Profiling dataset {size}")
print("Profiling loading")
print("=================")
print()
g = erdos_renyi_M(size, edge=m, directed=False).cpp()
print('*****************************')
print("Profiling shortest path")
print("=======================")
print()
# node_num: sample node for dijkstra
node_num = 1000
start_idx, end_idx = 0, len(g.nodes)-1
random_node_index_list = random_nodes(node_num, start_idx, end_idx)
nodes = list(g.nodes)
eg_node_list = []
for index in random_node_index_list:
eg_node_list.append(nodes[index])
# print("eg_node_list:",eg_node_list)
benchmark('multi_source_dijkstra(g, sources = eg_node_list)', globals=globals(), n=n)
# pagerank only apply for directed graph
# print("Profiling pagerank")
# print("=======================")
# print()
# benchmark('eg.pagerank(g,alpha=0.85)', globals=globals(), n=n)
print("Profiling k-core")
print("=======================")
print()
benchmark('eg.k_core(g)', globals=globals(), n=n)
print("Profiling closeness centrality")
print("=======================")
print()
benchmark('eg.closeness_centrality(g)', globals=globals(), n=n)
print("Profiling betweenness centrality")
print("=======================")
print()
benchmark('eg.betweenness_centrality(g)', globals=globals(), n=n)
# =======================igraph=======================
print(f"Profiling dataset {size}")
print("Profiling loading")
print("=================")
print()
benchmark('ig.Graph().Erdos_Renyi(n=size, m=m, directed=False)', globals=globals(), n=n)
g = ig.Graph().Erdos_Renyi(n=size, m=m, directed=False)
print(len(g.vs),len(g.es))
print("Profiling shortest path")
print("=======================")
print()
ig_node_list = [int(i) for i in eg_node_list]
benchmark("g.distances(source = ig_node_list,weights=[1]*len(g.es))", globals=globals(), n=n)
# pagerank only apply for directed graph
print("Profiling pagerank")
print("=======================")
print()
benchmark('g.pagerank(damping=0.85)', globals=globals(), n=n)
print("Profiling k-core")
print("=======================")
print()
benchmark('g.coreness()', globals=globals(), n=n)
print("Profiling closeness")
print("=======================")
print()
benchmark('g.closeness(weights=[1]*len(g.es))', globals=globals(), n=n)
print("Profiling betweenness")
print("=======================")
print()
benchmark('g.betweenness(directed=False,weights=[1]*len(g.es))', globals=globals(), n=n)