def __init__(self, operationCount, peerCount, graphInput):
print("Inside Simulation init")
self._op_count = int(operationCount)
self._peer_count = int(peerCount)
plt.axis('off')
graph = nx.read_weighted_edgelist(graphInput)
print(graph)
# some properties
print("node degree clustering")
for v in nx.nodes(graph):
print('%s %d %f' % (v, nx.degree(graph, v), nx.clustering(graph, v)))
# print the adjacency list to terminal
try:
nx.write_adjlist(graph, sys.stdout)
except TypeError:
nx.write_adjlist(graph, sys.stdout.buffer)
# node_pos = nx.spring_layout(graph)
#
# edge_weight = nx.get_edge_attributes(graph, 'weight')
# # Draw the nodes
# nx.draw_networkx(graph, node_pos, node_color='grey', node_size=100)
# # Draw the edges
# nx.draw_networkx_edges(graph, node_pos, edge_color='black')
# # Draw the edge labels
# nx.draw_networkx_edge_labels(graph, node_pos, edge_color='red', edge_labels=edge_weight)
# plt.show()
paths_for_diameter = nx.shortest_path_length(graph, weight='weight')
ecc = nx.eccentricity(graph, sp=dict(paths_for_diameter))
self._diameter = nx.diameter(graph, e=ecc)
print('The graph diameter is ', self._diameter)
self._height_of_cluster = math.ceil(math.log(self._diameter, 2)) + 1
if not os.path.exists(graphInput+'_network'):
print("CREATING NEW NETWORK")
self._network = Network(graph)
self._setup_peers(graph)
# self._build_clusters_exactly_logn_membership(graph)
self._build_clusters_at_least_one_and_at_most_logn(graph)
# self._build_clusters_no_overlapping(graph)
self._build_tree(graph)
self.save_network(graphInput + '_network')
exit(0)
else:
print("LOADING NETWORK FROM INPUT FILE")
# load network
self.load_network(graphInput + '_network')
source_node = next(
iter(sorted(nx.connected_components(G), key=len, reverse=True)[i]))
dest_node = next(
iter(sorted(nx.connected_components(G), key=len, reverse=True)[0]))
print("SOURCE NODEE", source_node)
print("DEST NODEE", dest_node)
G.add_edge(int(source_node), int(dest_node), weight=randint(1, 1))
assert nx.is_connected(G)
print(sorted(sorted(nx.connected_components(G), key=len, reverse=True)))
# some properties
print("node degree clustering")
for v in nx.nodes(G):
print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v)))
# G.add_edge(0, 3, weight=4)
for e in G.edges:
a = e[0]
b = e[1]
w = randint(1, 1)
G.add_edge(int(e[0]), int(e[1]), weight=w)
# print (a)
# Drawing the graph
node_pos = nx.spring_layout(G)
edge_weight = nx.get_edge_attributes(G, 'weight')
# Copyright (C) 2004-2017 by
# Aric Hagberg <*****@*****.**>
# Dan Schult <*****@*****.**>
# Pieter Swart <*****@*****.**>
# All rights reserved.
# BSD license.
import sys
import matplotlib.pyplot as plt
from networkx import nx
n = 10 # 10 nodes
m = 20 # 20 edges
G = nx.gnm_random_graph(n, m)
# some properties
print("node degree clustering")
for v in nx.nodes(G):
print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v)))
# print the adjacency list to terminal
try:
nx.write_adjlist(G, sys.stdout)
except TypeError: # Python 3.x
nx.write_adjlist(G, sys.stdout.buffer)
nx.draw(G)
plt.show()
import sys
import matplotlib.pyplot as plt
from networkx import nx, json_graph
plt.axis('off')
# graph = nx.read_gml("test_gml")
graph = nx.read_weighted_edgelist("8_0.01diamter3_newtest.weighted.edgelist")
# some properties
print("node degree clustering")
for v in nx.nodes(graph):
print('%s %d %f' % (v, nx.degree(graph, v), nx.clustering(graph, v)))
# print the adjacency list to terminal
try:
nx.write_adjlist(graph, sys.stdout)
except TypeError:
nx.write_adjlist(graph, sys.stdout.buffer)
node_pos = nx.spring_layout(graph)
edge_weight = nx.get_edge_attributes(graph, 'weight')
# Draw the nodes
nx.draw_networkx(graph, node_pos, node_color='grey', node_size=100)
# Draw the edges
nx.draw_networkx_edges(graph, node_pos, edge_color='black')
# Draw the edge labels