def iso_graphs(version=2):
# Return the list of all graphs with up to seven nodes named in the Graph Atlas.
if version == 1:
graphs = []
n = len(nx.graph_atlas_g())
for i in range(n):
graphs.append([nx.graph_atlas(i)])
return graphs
elif version == 2:
graphs = nx.graph_atlas_g()
graphs = [[g] for g in graphs]
return graphs[1:]
def atlas6():
""" Return the atlas of all connected graphs of 6 nodes or less.
Attempt to check for isomorphisms and remove.
"""
Atlas=nx.graph_atlas_g()[0:208] # 208
# remove isolated nodes, only connected graphs are left
U=nx.Graph() # graph for union of all graphs in atlas
for G in Atlas:
zerodegree=[n for n in G if G.degree(n)==0]
for n in zerodegree:
G.remove_node(n)
U=nx.disjoint_union(U,G)
# list of graphs of all connected components
C=nx.connected_component_subgraphs(U)
UU=nx.Graph()
# do quick isomorphic-like check, not a true isomorphism checker
nlist=[] # list of nonisomorphic graphs
for G in C:
# check against all nonisomorphic graphs so far
if not iso(G,nlist):
nlist.append(G)
UU=nx.disjoint_union(UU,G) # union the nonisomorphic graphs
return UU
def atlas6():
""" Return the atlas of all connected graphs of 6 nodes or less.
Attempt to check for isomorphisms and remove.
"""
Atlas = nx.graph_atlas_g()[0:208] # 208
# remove isolated nodes, only connected graphs are left
U = nx.Graph() # graph for union of all graphs in atlas
for G in Atlas:
zerodegree = [n for n in G if G.degree(n) == 0]
for n in zerodegree:
G.remove_node(n)
U = nx.disjoint_union(U, G)
# list of graphs of all connected components
C = nx.connected_component_subgraphs(U)
UU = nx.Graph()
# do quick isomorphic-like check, not a true isomorphism checker
nlist = [] # list of nonisomorphic graphs
for G in C:
# check against all nonisomorphic graphs so far
if not iso(G, nlist):
nlist.append(G)
UU = nx.disjoint_union(UU, G) # union the nonisomorphic graphs
return UU
def count_exact(queries, targets, args):
print("WARNING: orca only works for node anchored")
# TODO: non node anchored
n_matches_baseline = np.zeros(73)
for target in targets:
counts = np.array(orca.orbit_counts("node", 5, target))
if args.count_method == "bin":
counts = np.sign(counts)
counts = np.sum(counts, axis=0)
n_matches_baseline += counts
# don't include size < 5
n_matches_baseline = list(n_matches_baseline)[15:]
counts5 = []
num5 = 10 #len([q for q in queries if len(q) == 5])
for x in list(sorted(n_matches_baseline, reverse=True))[:num5]:
print(x)
counts5.append(x)
print("Average for size 5:", np.mean(np.log10(counts5)))
atlas = [
g for g in nx.graph_atlas_g()[1:] if nx.is_connected(g) and len(g) == 6
]
queries = []
for g in atlas:
for v in g.nodes:
g = g.copy()
nx.set_node_attributes(g, 0, name="anchor")
g.nodes[v]["anchor"] = 1
is_dup = False
for g2 in queries:
if nx.is_isomorphic(
g,
g2,
node_match=(lambda a, b: a["anchor"] == b["anchor"])
if args.node_anchored else None):
is_dup = True
break
if not is_dup:
queries.append(g)
print(len(queries))
n_matches_baseline = count_graphlets(queries,
targets,
n_workers=args.n_workers,
method=args.count_method,
node_anchored=args.node_anchored,
min_count=10000)
counts6 = []
num6 = 20 #len([q for q in queries if len(q) == 6])
for x in list(sorted(n_matches_baseline, reverse=True))[:num6]:
print(x)
counts6.append(x)
print("Average for size 6:", np.mean(np.log10(counts6)))
return counts5 + counts6
def test_graph_atlas():
#Atlas = nx.graph_atlas_g()[0:208] # 208, 6 nodes or less
Atlas = nx.graph_atlas_g()[0:100]
alphabet = range(26)
for graph in Atlas:
nlist = graph.nodes()
labels = alphabet[:len(nlist)]
for s in range(10):
random.shuffle(labels)
d = dict(zip(nlist,labels))
relabel = nx.relabel_nodes(graph, d)
gm = vf2.GraphMatcher(graph, relabel)
assert_true(gm.is_isomorphic())
def atlas6():
"""Return the atlas of all connected graphs with at most 6 nodes"""
Atlas = nx.graph_atlas_g()[
3:209] # 0, 1, 2 => no edges. 208 is last 6 node graph
U = nx.Graph() # graph for union of all graphs in atlas
for G in Atlas:
# check if connected
if nx.number_connected_components(G) == 1:
# check if isomorphic to a previous graph
if not GraphMatcher(U, G).subgraph_is_isomorphic():
U = nx.disjoint_union(U, G)
return U
def test_graph_atlas():
#Atlas = nx.graph_atlas_g()[0:208] # 208, 6 nodes or less
Atlas = nx.graph_atlas_g()[0:100]
alphabet = list(range(26))
for graph in Atlas:
nlist = graph.nodes()
labels = alphabet[:len(nlist)]
for s in range(10):
random.shuffle(labels)
d = dict(zip(nlist, labels))
relabel = nx.relabel_nodes(graph, d)
gm = iso.GraphMatcher(graph, relabel)
assert_true(gm.is_isomorphic())
def load_dataset(name):
""" Load real-world datasets, available in PyTorch Geometric.
Used as a helper for DiskDataSource.
"""
task = "graph"
if name == "enzymes":
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
elif name == "proteins":
dataset = TUDataset(root="/tmp/PROTEINS", name="PROTEINS")
elif name == "cox2":
dataset = TUDataset(root="/tmp/cox2", name="COX2")
elif name == "aids":
dataset = TUDataset(root="/tmp/AIDS", name="AIDS")
elif name == "reddit-binary":
dataset = TUDataset(root="/tmp/REDDIT-BINARY", name="REDDIT-BINARY")
elif name == "imdb-binary":
dataset = TUDataset(root="/tmp/IMDB-BINARY", name="IMDB-BINARY")
elif name == "firstmm_db":
dataset = TUDataset(root="/tmp/FIRSTMM_DB", name="FIRSTMM_DB")
elif name == "dblp":
dataset = TUDataset(root="/tmp/DBLP_v1", name="DBLP_v1")
elif name == "ppi":
dataset = PPI(root="/tmp/PPI")
elif name == "qm9":
dataset = QM9(root="/tmp/QM9")
elif name == "atlas":
dataset = [g for g in nx.graph_atlas_g()[1:] if nx.is_connected(g)]
if task == "graph":
train_len = int(0.8 * len(dataset))
train, test = [], []
dataset = list(dataset)
random.shuffle(dataset)
has_name = hasattr(dataset[0], "name")
for i, graph in tqdm(enumerate(dataset)):
if not type(graph) == nx.Graph:
if has_name: del graph.name
graph = pyg_utils.to_networkx(graph).to_undirected()
if i < train_len:
train.append(graph)
else:
test.append(graph)
return train, test, task
def load_dataset(name):
""" Load real-world datasets, available in PyTorch Geometric.
Used as a helper for DiskDataSource.
"""
task = "graph"
if name == "enzymes":
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
elif name == "proteins":
dataset = TUDataset(root="/tmp/PROTEINS", name="PROTEINS")
elif name == "cox2":
dataset = TUDataset(root="/tmp/cox2", name="COX2")
elif name == "aids":
dataset = TUDataset(root="/tmp/AIDS", name="AIDS")
elif name == "reddit-binary":
dataset = TUDataset(root="/tmp/REDDIT-BINARY", name="REDDIT-BINARY")
elif name == "imdb-binary":
dataset = TUDataset(root="/tmp/IMDB-BINARY", name="IMDB-BINARY")
elif name == "firstmm_db":
dataset = TUDataset(root="/tmp/FIRSTMM_DB", name="FIRSTMM_DB")
elif name == "dblp":
dataset = TUDataset(root="/tmp/DBLP_v1", name="DBLP_v1")
elif name == "ppi":
dataset = PPI(root="/tmp/PPI")
elif name == "qm9":
dataset = QM9(root="/tmp/QM9")
elif name == "atlas":
dataset = [g for g in nx.graph_atlas_g()[1:] if nx.is_connected(g)]
elif name == 'aifb':
dataset = Entities(root="/tmp/aifb", name='AIFB') # 90 edge types
elif name == 'wn18':
dataset = WordNet18(root="/tmp/wn18")
elif name == 'fb15k237':
dataset = [None]
if task == "graph":
train_len = int(0.8 * len(dataset))
train, test = [], []
if name not in ['aifb', 'wn18', 'fb15k237']:
dataset = list(dataset)
random.shuffle(dataset)
has_name = hasattr(dataset[0], "name")
else:
has_name = True
for i, graph in tqdm(enumerate(dataset)):
if not type(graph) == nx.Graph:
try:
if has_name: del graph.name
except:
pass
if name == 'aifb':
graph = pyg_utils.to_networkx(graph,
edge_attrs=['edge_type'])
elif name == 'wn18':
graph = pyg_utils.to_networkx(graph,
edge_attrs=['edge_type'])
elif name == 'fb15k237':
data = FB15k_237()
(graph, _, _, _) = data.load()
graph = graph.to_networkx()
edge_type_dict = []
for j in graph.edges:
edge_type_dict.append(graph.edges[j]['label'])
edge_type_dict = {
i: ind
for ind, i in enumerate(sorted(set(edge_type_dict)))
}
for j in graph.edges:
graph.edges[j]['edge_type'] = edge_type_dict[
graph.edges[j]['label']]
del graph.edges[j]['label']
del graph.edges[j]['weight']
else:
graph = pyg_utils.to_networkx(graph).to_undirected()
if name == 'aifb':
train.append(graph)
test.append(deepcopy(graph))
elif name == 'wn18':
train.append(graph)
test.append(deepcopy(graph))
elif name == 'fb15k237':
train.append(graph)
test.append(deepcopy(graph))
else:
if i < train_len:
train.append(graph)
else:
test.append(graph)
return train, test, task
def generate_small_graphs(max_size):
return [
g for g in nx.graph_atlas_g()
if len(g) > 1 and len(g) <= max_size and nx.is_connected(g)
]
def setup_class(cls):
cls.GAG = graph_atlas_g()
def setUp(self):
self.GAG=nx.graph_atlas_g()
def setUp(self):
self.GAG = nx.graph_atlas_g()
plt.show()
#%%
#%%
import pandas as pd
import numpy as np
price = pd.Series(np.random.randn(150).cumsum(),
index=pd.date_range('2000-1-1', periods=150, freq='B'))
ma = price.rolling(20).mean()
mstd = price.rolling(20).std()
plt.figure()
plt.plot(price.index, price, 'k')
plt.plot(ma.index, ma, 'b')
plt.fill_between(mstd.index,
ma - 2 * mstd,
ma + 2 * mstd,
color='b',
alpha=0.2)
plt.show()
#%%
import networkx as nx
GAG = nx.graph_atlas_g()
nx.draw(GAG[123])
plt.show()
"""
Atlas
=====
An example showing how to write first 20 graphs from the graph atlas as
graphviz dot files Gn.dot where n=0,19.
TODO: does nx_agraph.draw support multiple graphs in one png?
"""
import networkx as nx
atlas = nx.graph_atlas_g()[0:20]
for G in atlas:
print(G)
A = nx.nx_agraph.to_agraph(G)
A.graph_attr["label"] = G.name
# set default node attributes
A.node_attr["color"] = "red"
A.node_attr["style"] = "filled"
A.node_attr["shape"] = "circle"
A.write(G.name + ".dot")
# Draw the 20th graph from the atlas to png
A.draw("A20.png", prog="neato")
def test_atlas():
for graph in nx.graph_atlas_g():
deg = list(graph.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test_atlas():
for graph in nx.graph_atlas_g():
deg = list(graph.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
import networkx as nx
from classical import brute_force
# Defining classical optimizer necessary for QAOA
optimizer = optimizers.SPSA()
# Backend
backend = Aer.get_backend('qasm_simulator') #simulator
# Producing the data
p_min, p_max = 1, 3
# Putting the whole atlas in one data frame
output = pd.DataFrame()
for i in range(3, len(nx.graph_atlas_g())):
G = nx.graph_atlas(i)
n = len(G.nodes)
m = len(G.edges)
d = m / (n * (n - 1) / 2
) # density of graph - number of edges / number of possible edges
optimum = brute_force(G)
print("\nGraph " + str(i) + ": n = " + str(n), ", m = " + str(m),
", d = " + str(d), "the optimum cut is " + str(optimum) + "\n")
if m > 0: # Method does not work if there are no edges
for p in range(p_min, p_max + 1):
# specifications
specs = {
'graph_atlas index': i,
