def _construct_graph_objs():
graphs = []
for graph_type in [nx.Graph, nx.MultiGraph, nx.DiGraph, nx.MultiDiGraph]:
g1 = nx.from_edgelist([("2", "1"), ("3", "1"), ("1", "0")],
create_using=graph_type)
g2 = g1.copy()
attrs = {
n: {
l: i
}
for i, (n, l) in enumerate(zip(g2.nodes, string.ascii_lowercase))
}
nx.set_node_attributes(g2, attrs)
g3 = copy.deepcopy(g2)
attrs = {
e: {
l: i
}
for i, (e, l) in enumerate(zip(g2.edges, string.ascii_lowercase))
}
nx.set_edge_attributes(g3, attrs)
graphs.extend([(g1, True), (g2, True), (g3, True)])
graphs.extend([
# one out edge; these are 'valid' in `nereid`
(nx.gn_graph(25, seed=42), True),
# many out edges; these are not 'valid' in `nereid`
(nx.gnc_graph(25, seed=42), False),
])
return graphs
def RandomDirectedGNC(self, n, seed=None):
"""
Returns a random GNC (growing network with copying) digraph with n
vertices.
The digraph is constructed by adding vertices with a link to one
previously added vertex. The vertex to link to is chosen with a
preferential attachment model, i.e. probability is proportional to
degree. The new vertex is also linked to all of the previously
added vertex's successors.
INPUT:
- ``n`` - number of vertices.
- ``seed`` - for the random number generator
EXAMPLE::
sage: D = digraphs.RandomDirectedGNC(25)
sage: D.edges(labels=False)
[(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
sage: D.show() # long time
REFERENCE:
- [1] Krapivsky, P.L. and Redner, S. Network Growth by
Copying, Phys. Rev. E vol. 71 (2005), p. 036118.
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return DiGraph(networkx.gnc_graph(n, seed=seed))
def test_hmc_loss_speed(self):
print("\nCalculation time test begins")
data_sizes = [25, 100, 400]
node_sizes = [50, 200, 400]
for data_size in data_sizes:
for node_size in node_sizes:
true_label = np.random.randint(2, size=(data_size,node_size))
pred_label = np.random.randint(2, size=(data_size,node_size))
graph = nx.gnc_graph(node_size)
label_list = list(range(node_size))
start_time = time.time()
cost_list = get_cost_list(graph, 0, label_list)
cost_list_time = time.time()
hmc_loss_score(true_label,
pred_label,
graph,
0,
label_list,
cost_list)
end_time = time.time()
whole_calc_time = end_time - start_time
cost_calc_time = cost_list_time - start_time
hmc_calc_time = end_time - cost_list_time
print("Whole calc. time:{0}, Cost list calc. time:{1}, HMC-loss calc. time:{2}, data size:{3}, node size:{4}".format(whole_calc_time, cost_calc_time, hmc_calc_time, data_size, node_size))
return 0
def RandomDirectedGNR(self, n, p, seed=None):
"""
Returns a random GNR (growing network with redirection) digraph
with n vertices and redirection probability p.
The digraph is constructed by adding vertices with a link to one
previously added vertex. The vertex to link to is chosen uniformly.
With probability p, the arc is instead redirected to the successor
vertex. The digraph is always a tree.
INPUT:
- ``n`` - number of vertices.
- ``p`` - redirection probability
- ``seed`` - for the random number generator.
EXAMPLE::
sage: D = digraphs.RandomDirectedGNR(25, .2)
sage: D.edges(labels=False)
[(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
sage: D.show() # long time
REFERENCE:
- [1] Krapivsky, P.L. and Redner, S. Organization of Growing
Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return DiGraph(networkx.gnc_graph(n, seed=seed))
def RandomDirectedGNR(self, n, p, seed=None):
"""
Returns a random GNR (growing network with redirection) digraph
with n vertices and redirection probability p.
The digraph is constructed by adding vertices with a link to one
previously added vertex. The vertex to link to is chosen uniformly.
With probability p, the arc is instead redirected to the successor
vertex. The digraph is always a tree.
INPUT:
- ``n`` - number of vertices.
- ``p`` - redirection probability
- ``seed`` - for the random number generator.
EXAMPLE::
sage: D = digraphs.RandomDirectedGNR(25, .2)
sage: D.edges(labels=False)
[(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
sage: D.show() # long time
REFERENCE:
- [1] Krapivsky, P.L. and Redner, S. Organization of Growing
Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return DiGraph(networkx.gnc_graph(n, seed=seed))
def RandomDirectedGNC(self, n, seed=None):
"""
Returns a random GNC (growing network with copying) digraph with n
vertices.
The digraph is constructed by adding vertices with a link to one
previously added vertex. The vertex to link to is chosen with a
preferential attachment model, i.e. probability is proportional to
degree. The new vertex is also linked to all of the previously
added vertex's successors.
INPUT:
- ``n`` - number of vertices.
- ``seed`` - for the random number generator
EXAMPLE::
sage: D = digraphs.RandomDirectedGNC(25)
sage: D.edges(labels=False)
[(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
sage: D.show() # long time
REFERENCE:
- [1] Krapivsky, P.L. and Redner, S. Network Growth by
Copying, Phys. Rev. E vol. 71 (2005), p. 036118.
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return DiGraph(networkx.gnc_graph(n, seed=seed))
def _construct_graphs():
graphs = [
# one out edge; these are 'valid' in `nereid`
(nx.gn_graph(25, seed=42)),
# many out edges; these are not 'valid' in `nereid`
(nx.gnc_graph(25, seed=42)),
]
return graphs
def test_invalid_graph(contexts):
context = contexts["default"]
g = nx.gnc_graph(10)
data = generate_random_watershed_solve_request_from_graph(
g,
context,
)
g, err = initialize_graph(data, False, context)
assert len(err) > 0
def graph_sel(graphType, n, p):
'''
Funzione che genera e restituisce un grafo del tipo prescelto.
Viene invocata solo se, nell'inizializzazione del repository,
non viene direttamente specifica un grafo ma solo un graph type.
'''
return {
'gn': lambda: nx.gn_graph(n),
'gnr': lambda: nx.gnr_graph(n, p),
'gnc': lambda: nx.gnc_graph(n),
'scale_free': lambda: nx.scale_free_graph(n),
'erdos_renyi': lambda: nx.erdos_renyi_graph(n, p, directed=True),
'nSCC_graph': lambda: self.nSCC_graph(n)
}.get(graphType, graphType)()
def create_random_followers_graph(followers_list,fil=None):
'''Creates a random_followers_graph with nodes the followers_list'''
if fil==None:
fil='friendship_graph.graphml'
n=len(followers_list)
G=nx.gnc_graph(n)
F=G.__class__()
mapping_f={}
for i,v in enumerate(followers_list):
mapping_f[i]=v
for edg in G.edges(data=True):
F.add_edge(mapping_f[edg[0]],mapping_f[edg[1]],attr_dict=edg[2])
for i in G.nodes():
att=G.node[i]
F.add_node(mapping_f[i], attr_dict=att)
nx.write_graphml(F,fil)
def directed_graphs():
print("Directed graphs")
print("Growing network")
D = nx.gn_graph(10) # the GN graph
draw_graph(D)
G = D.to_undirected() # the undirected version
draw_graph(G)
D = nx.gn_graph(10, kernel=lambda x: x**1.5) # A_k = k^1.5
draw_graph(D)
print("Growing network graph")
D = nx.gnr_graph(n=11, p=0.3)
draw_graph(D)
G = D.to_undirected()
draw_graph(G)
print("Growing network with copying graph")
D = nx.gnc_graph(n=7)
draw_graph(D)
G = D.to_undirected()
draw_graph(G)
print("Scale-free graph")
G = nx.scale_free_graph(10)
draw_graph(G)
def named_validation_responses(client):
route = API_LATEST + "/network/validate"
responses = {}
slow_valid = json.dumps(
clean_graph_dict(nx.gnr_graph(15000, p=0.05, seed=42)))
slow_invalid = json.dumps(clean_graph_dict(nx.gnc_graph(15000, seed=42)))
init_post_requests = [
("valid_graph_response_fast",
get_payload("network_validate_is_valid.json")),
(
"invalid_graph_response_fast",
get_payload("network_validate_is_invalid_cycle.json"),
),
("valid_graph_response_slow", slow_valid),
("invalid_graph_response_slow", slow_invalid),
]
for name, payload in init_post_requests:
response = client.post(route, data=payload)
responses[name] = response
yield responses
import random
import itertools
import numpy
import scipy
import networkx as nx
import matplotlib.pyplot as plt
N = 10
"""
G = nx.DiGraph()
for u in range(n):
for v in range(u+1, n):
if random.random() < 0.8*u/n:
G.add_edge(u, v)
"""
G = nx.gnc_graph(N)
A = nx.to_scipy_sparse_matrix(G, dtype=float)
Nu = scipy.sparse.linalg.norm(A, ord=0, axis=1)
Nu = (1 / Nu)
Nu[numpy.isinf(Nu)] = 0
A = A.T.multiply(Nu).T
print(A.todense())
eigvals, eigvecs = numpy.linalg.eig(A.todense())
print(eigvals)
plt.figure(figsize=(10, 10))
pos = nx.spring_layout(G)
nx.draw_networkx_labels(G, pos)
nx.draw(G, pos)
def test_voterank_centrality_3(self):
G = nx.gnc_graph(10, seed=7)
d = nx.voterank(G, 4)
exact = [3, 6, 8]
assert exact == d
(
[["a", "b"], ["b", "c"], ["d", "c"], ["c", "e"], ["c", "e"]],
False,
([], [], [["c", "2"]], [["c", "e"]]),
), # duplicate edge
(
[["a", "b"], ["b", "c"], ["d", "c"], ["a", "e"]],
False,
([], [], [["a", "2"]], []),
), # multiple out edges, a->b & a->e
],
)
def test_validate_network(edgelist, isvalid, result):
g = nx.from_edgelist(edgelist, create_using=nx.MultiDiGraph)
assert isvalid == is_valid(g)
assert result == validate_network(g)
@pytest.mark.parametrize(
"g, expected",
[ # multiple out connections
(nx.gnc_graph(10, seed=42), False),
# multiple out connections, cycles, duplicated edges
(nx.random_k_out_graph(10, 2, 1, seed=42), False),
(nx.gn_graph(10, seed=42), True),
],
)
def test_isvalid(g, expected):
assert expected == is_valid(g)
# nx.draw(g) # drawing the Graph # nx.draw_circular(g) # to draw the graph in circular form # nx.draw_spring(g) # to draw the graph in spring form # plt.show() # plotting the graph # Commands for Labeling and Coloring the Graph # nx.draw_circular(g, node_color='bisque', with_labels='TRUE') # to draw the graph with bisque color and nodes marked with with_labels # Command to show the stats of the Graph # stats = nx.degree(g) # this method will give the number of nodes, edges, avg. degree of the graph # print stats # print nx.degree(g) # this methood will display the degree of each node in the graph # Graph Generators Functions # g = nx.complete_graph(25) # this method will make a complete graph with 25 nodes # nx.draw(g, node_color='bisque', with_labels='TRUE') g = nx.gnc_graph(7, seed=25) # creating a random graph with GNC function # nx.draw(g, node_color='bisque', with_labels='TRUE') ego_g = nx.ego_graph(g, 3, radius=5) # creating a ego graph as a sub graph to g, 3 is the center nodes and radius of the graph is 5 nx.draw(ego_g, node_color='bisque', with_labels='TRUE') plt.show()
#%% # Removing nodes G.remove_node(1) nx.draw_circular(G, node_color='bisque', with_labels=True) #%% # Graph properties # Show the stats. sum_stats = nx.info(G) print(sum_stats) #%% # CLoser views print(nx.degree(G)) #%% # An automatic graph generator, NO manual inputs needed. G = nx.complete_graph(25) nx.draw(G, node_color='bisque', with_labels=True) #%% G = nx.gnc_graph(7, seed=25) nx.draw(G, node_color='bisque', with_labels=True) #%% # Ego graph, Social networks use these. ego_G = nx.ego_graph(G, 3, radius=5) nx.draw(G, node_color='bisque', with_labels=True) #%%
(8, 16)]) nx.draw(G) nx.draw_circular(G, node_color='bisque', with_labels=True) nx.draw_spring(G) G.remove_node(1) sum_stats = nx.info(G) print(sum_stats) print(nx.degree(G)) G = nx.complete_graph(25) nx.draw(G, node_color='bisque', with_labels=True) G = nx.gnc_graph(7, seed=25) #directed graph nx.draw(G, node_color='bisque', with_labels=True) ego_G = nx.ego_graph(G, 3, radius=5) nx.draw(G, node_color='bisque', with_labels=True) #============================================================================= # Social network # generate a graph object and edgelist DG = nx.gn_graph(7, seed=25) #for line in nx.generate_edgelist(DG,data=False):print(line) # assign attributes to nodes DG.node[0]['name'] = 'Alice'
