def delete_nodes(G, type='deg', percent_remove=0.01):
total_nodes = G.number_of_nodes()
remove_count = int(percent_remove * total_nodes)
print(' Adversary removes {} nodes'.format(remove_count))
if type == 'deg':
deg = dict(G.degree())
deg_sorted = _mylib.sortDictByValues(deg, reverse=True)
l = deg_sorted
elif type == 'pr':
pr = dict(nx.pagerank(G))
pr_sorted = _mylib.sortDictByValues(pr, reverse=True)
l = pr_sorted
elif type == 'betweeness':
betweeness = dict(nx.betweenness_centrality(G))
betweeness_sorted = _mylib.sortDictByValues(betweeness, reverse=True)
l = betweeness_sorted
G_copy = G.copy()
for idx, i in enumerate(l):
if idx == remove_count:
break
node = i[0]
d = i[1]
G_copy.remove_node(node)
print(' {} Remove node {}'.format(idx, node))
return G_copy
def delete_hub_nodes_connect(G, percent_remove=.01):
total_node = G.number_of_nodes()
remove_count = int(percent_remove * total_node)
print('Remove total:' + str(remove_count))
G_copy = G.copy()
i = 0
possible_nodes = set(G_copy.nodes())
while i < remove_count:
deg = dict(G_copy.degree(possible_nodes))
deg_sorted = _mylib.sortDictByValues(deg, reverse=True)
selected_node = deg_sorted[0][0]
G_tmp = G_copy.copy()
G_tmp.remove_node(selected_node)
isConnected = nx.is_connected(G_tmp)
if isConnected:
G_copy.remove_node(selected_node)
i += 1
else:
print('Cannot remove this node \t {} / {}'.format(i, remove_count))
possible_nodes.remove(selected_node)
return G_copy
def randomFromLargeCommunity(self, G, dataset):
com_fname = './data/pickle/communities_{}.pickle'.format(dataset)
if os.path.isfile(com_fname):
partition = pickle.load(open(com_fname, 'rb'))
else:
partition = community.best_partition(G)
pickle.dump(partition, open(com_fname, 'wb'))
count_members = {}
for p in set(partition.values()):
members = _mylib.get_members_from_com(p, partition)
count_members[p] = len(members)
selected_p, i = _mylib.get_max_values_from_dict(
count_members, count_members.keys())
members = _mylib.get_members_from_com(selected_p, partition)
degree = self._graph.degree(members)
degree_sorted = _mylib.sortDictByValues(degree, reverse=True)
size = int(.5 * len(degree))
degree_sorted = degree_sorted[:size]
return random.choice(degree_sorted)[0]
def randomHighDegreeNode(self):
degree = self._graph.degree()
degree_sorted = _mylib.sortDictByValues(degree, reverse=True)
size = int(.3 * len(degree))
degree_sorted = degree_sorted[:size]
return random.choice(degree_sorted)[0]