def __createSyPyNetwork__(nodes_val, id_to_edges):
""" Internal. Converts a given simulation into a SyPy-friendly 'network'
object that allows utilizing other (GSD-based) detection algorithms.
Finds the largest connected component, and then creates a graph out of
the bidirectional edges and corresponding nodes. """
all_ids = {node.id for node in nodes_val}
hon_ids = {node.id for node in nodes_val if node.type == "hon"}
syb_ids = [node.id for node in nodes_val if node.type == "syb"]
mal_ids = [node.id for node in nodes_val if node.type == "mal"]
succ_edges = set()
bidir_edges = []
for node_id in all_ids:
for edge in id_to_edges[node_id]:
if edge.successful:
src_id = edge.node_src.id
dst_id = edge.node_dst.id
if (dst_id, src_id) in succ_edges:
bidir_edges += [(dst_id, src_id)]
else:
succ_edges.add((src_id, dst_id))
nx_graph = nx.Graph()
nx_graph.add_nodes_from(all_ids)
nx_graph.add_edges_from(bidir_edges)
cc_graph = max(nx.connected_component_subgraphs(nx_graph), key=len)
cc_nodes = cc_graph.nodes()
cc_edges = []
for edge in bidir_edges:
if edge[0] in cc_nodes and edge[1] in cc_nodes:
cc_edges += [edge]
nx_graph = nx.Graph()
nx_graph.add_nodes_from(cc_nodes)
nx_graph.add_edges_from(cc_edges)
initial_sybils = list(set(all_ids) - set(cc_nodes))
honest_edge_counts = {node_id: 0 for node_id in cc_nodes}
for edge in cc_edges:
if edge[0] in hon_ids and edge[1] in hon_ids:
honest_edge_counts[edge[0]] += 1
honest_edge_counts[edge[1]] += 1
known_honests = [
sorted(honest_edge_counts.items(),
key=lambda kv: kv[1],
reverse=True)[0][0]
]
network = sypy.Network(None,
None,
"test",
custom_graph=sypy.CustomGraph(nx_graph))
network.set_data(original_nodes=all_ids,
sybils=syb_ids,
malicious=mal_ids,
known_honests=known_honests,
initial_sybils=initial_sybils)
return network
#create honest region
honest_region = sypy.Region(graph=sypy.PowerLawGraph(num_nodes=100,
node_degree=2,
prob_triad=0.03,
seed=1),
name="HonestPowerLawGraph")
honest_region.pick_random_honest_nodes(num_nodes=10)
nx.set_node_attributes(honest_region.graph.structure, 'Node_type',
'Honest_node')
#print honest_region.graph.structure.nodes(data='True')
#honest_region.visualize()
#create online scial network
social_network = sypy.Network(left_region=honest_region,
right_region=sybil_region,
name="OnlineSocialNetwork")
social_network.random_pair_stitch(num_edges=10)
social_network.visualize()
G = social_network.graph.structure
#print G.nodes(data='True')
nx.draw(G)
plt.show()
#create edgelist
nx.write_edgelist(G, "edgelistfinal.txt")
#find the diameter and radius
dia = nx.diameter(G, e=None)
#print (dia)
for node in honest_region.graph.nodes():
degree = honest_region.graph.structure.degree(node)
if degree > max_degree:
max_degree = degree
max_node = node
honest_region.pick_random_honest_nodes(num_nodes=49)
if max_node in honest_region.known_honests:
raise Exception("Max degree node already exists")
honest_region.known_honests += [max_node]
honest_stats = honest_region.get_region_stats()
assert honest_stats.is_connected == True
social_network = sypy.Network(left_region=honest_region,
right_region=sybil_region,
name="OnlineSocialNetwork",
seed=GLOBAL_SEED)
multi_benchmark = sypy.MultipleDetectorsBenchmark(
detectors=[sypy.SybilRankDetector, sypy.SybilPredictDetector],
network=social_network,
thresholds=["pivot", "pivot"],
kwargs=[{
"total_trust": social_network.graph.order(),
"num_iterations_scaler": 1.0,
"seed": GLOBAL_SEED
}, {
"total_trust": social_network.graph.order(),
"num_iterations_scaler": 1.0,
"operation_mode": "best",
"seed": GLOBAL_SEED