def create_test_graph(n: int, seed: int) -> nx.Graph:
internet: nx.Graph = nx.random_internet_as_graph(n, seed=seed)
node_types: List[str] = ['source', 'destination']
for i in range(n):
internet.add_node(i, type=random.choice(node_types))
return internet
def apply_elmokashfi_strategy(number_of_nodes: int,
seed: int = 1234,
out_name: str = "graph") -> nx.DiGraph:
G = nx.random_internet_as_graph(number_of_nodes, seed)
nx.write_graphml(G, out_name + "_original.graphml")
plot_original(G, out_name)
G = nx.DiGraph(G)
#insert_random_destination(G)
correct_graph_data(G)
return G
def generate_graph_data(num_nodes, random_seed):
"""Generates a graph of 10k nodes with a 3D force layout
This function is unused but serves as an example of how the data in
this visualization was generated
"""
import networkx as nx # noqa
g = nx.random_internet_as_graph(num_nodes, random_seed)
node_positions = nx.fruchterman_reingold_layout(g, dim=3)
force_layout_df = pd.DataFrame.from_records(node_positions).transpose()
force_layout_df["group"] = [d[1]["type"] for d in g.nodes.data()]
force_layout_df.columns = ["x", "y", "z", "group"]
return force_layout_df
for (node, nodedata) in G.nodes.items():
if H.nodes[node]['cluster_label'] is None:
H.nodes[node]['cluster_label'] = cluster_number
# print(nodedata)
for i in nodedata['community']:
H.nodes[i]['cluster_label'] = cluster_number
cluster_number += 1
G = H.copy()
# Random Internet AS Graph
if args.generation[0] == 'internet-as':
G = nx.random_internet_as_graph(args.n * 5)
for node in range(0, args.n * 5):
if G.nodes[node]['type'] == 'C':
G.remove_node(node)
# Random Partition Graph
if args.generation[0] == 'partition':
communities = [0
] * args.k if args.communities is None else args.communities
if args.communities is None:
for i in range(0, args.k):
communities[i] = random.randint(args.c_min, args.c_max)
G = nx.random_partition_graph(communities,
args.p_in,
args.p_out,
from datetime import datetime, timedelta
result= 'bgp-data-path.csv'
#bw_details = 'BW_OF_LINKS.csv'
fp = open(result, 'a')
fp.write("Type, Prefix, S-AS, D-AS \n")
pfx = '10.0.0.0/8' ### initial prefix from which subnets for each node is derived ###########
net = ipaddress.ip_network(pfx)
N = int(sys.argv[1]) ## number of nodes in Internet AS graph
#P = int(sys.argv[2]) ### number of prefixes per node
G = nx.random_internet_as_graph(N)
#print (G.edges(), G.nodes())
prefix_graphs = {}
prefix_len = math.ceil(math.log(N,2))
#print (prefix_len)
subnets = list(net.subnets(prefixlen_diff=prefix_len))
node_prefixes = {}
for i in G.nodes():
node_prefixes[i] = str(subnets[i])
print (node_prefixes)
def build_graph(args):
""" Builds graph from user specified parameters or use defaults."""
# Build graph using networkx
if args.graph == 'karate':
print("\nReading in karate graph...")
G = nx.karate_club_graph()
elif args.graph == 'internet':
if args.nodes < 1000 or args.nodes > 5000:
args.nodes = 1000
print(
"\nSize for internet graph must be between 1000 and 5000.\nSetting size to 1000.\n"
)
print("\nReading in internet graph of size", args.nodes, "...")
G = nx.random_internet_as_graph(args.nodes)
elif args.graph == 'rand-reg':
if args.nodes < 1:
print(
"\nMust have at least one node in the graph.\nSetting size to 1000.\n"
)
if args.degree < 0 or args.degree >= args.nodes:
print(
"\nDegree must be between 0 and n-1. Setting size to min(4, n-1).\n"
)
args.degree = min(4, args.nodes - 1)
if args.degree * args.nodes % 2 == 1:
print("\nRequirement: n*d must be even.\n")
if args.degree > 0:
args.degree -= 1
print("\nSetting degree to", args.degree, "\n")
elif args.nodes - 1 > args.degree:
args.nodes -= 1
print("\nSetting nodes to", args.nodes, "\n")
else:
print("\nSetting nodes to 1000 and degree to 4.\n")
args.nodes = 1000
args.degree = 4
print("\nGenerating random regular graph...")
G = nx.random_regular_graph(args.degree, args.nodes)
elif args.graph == 'ER':
if args.nodes < 1:
print(
"\nMust have at least one node in the graph. Setting size to 1000.\n"
)
args.nodes = 1000
if args.prob < 0 or args.prob > 1:
print(
"\nProbability must be between 0 and 1. Setting prob to 0.25.\n"
)
args.prob = 0.25
print("\nGenerating Erdos-Renyi graph...")
G = nx.erdos_renyi_graph(args.nodes, args.prob)
elif args.graph == 'SF':
if args.nodes < 1:
print(
"\nMust have at least one node in the graph. Setting size to 1000.\n"
)
args.nodes = 1000
if args.new_edges < 0 or args.new_edges > args.nodes:
print("\nNumber of edges must be between 1 and n. Setting to 5.\n")
args.new_edges = 5
print("\nGenerating Barabasi-Albert scale-free graph...")
G = nx.barabasi_albert_graph(args.nodes, args.new_edges)
else:
print("\nReading in karate graph...")
G = nx.karate_club_graph()
return G
