def features_matrix(graph, anchors, use_dist=True, use_pgrs=True,
use_pgr=True, use_comm=False, use_comm_centr=False):
node_feats = []
n = len(graph)
if use_dist:
dists = nx.all_pairs_shortest_path_length(graph)
if use_pgr:
pageranks = nx.pagerank_numpy(graph)
if use_pgrs:
pgr_anchor = [anchored_pagerank(graph, anchor) for anchor in anchors]
if use_comm_centr:
communicability_centrality = nx.communicability_centrality(graph)
if use_comm:
communicability = nx.communicability(graph)
for node in graph.nodes():
assert node == len(node_feats)
feats = []
if use_dist:
feats += [dists[node][anchor] for anchor in anchors]
if use_pgrs:
feats += [pgr[node]*n for pgr in pgr_anchor]
if use_pgr:
feats.append(pageranks[node]*n)
if use_comm_centr:
feats.append(communicability_centrality[node])
if use_comm:
feats += [communicability[node][anchor] for anchor in anchors]
node_feats.append(np.array(feats))
return node_feats
def features_dict(graph, anchors, use_dist=True, use_pgrs=True,
use_pgr=True, use_comm=False, use_comm_centr=False):
node_feats = {}
n = len(graph)
if use_dist:
# dists = nx.all_pairs_shortest_path_length(graph)
dists = dists_to_anchors(graph, anchors)
if use_pgr:
pageranks = nx.pagerank_numpy(graph)
if use_pgrs:
# pgr_anchor = [anchored_pagerank(graph, anchor) for anchor in anchors]
pgr_anchor = pageranks_to_anchors(graph, anchors)
if use_comm_centr:
communicability_centrality = nx.communicability_centrality(graph)
if use_comm:
communicability = nx.communicability(graph)
for node in graph.nodes():
feats = []
if use_dist:
feats += [dists[node][anchor] for anchor in anchors]
if use_pgrs:
feats += [pgr_anchor[anchor][node]*n
for anchor in range(len(anchors))]
# feats += [pgr[node]*n for pgr in pgr_anchor]
if use_pgr:
feats.append(pageranks[node]*n)
if use_comm_centr:
feats.append(communicability_centrality[node])
if use_comm:
feats += [communicability[node][anchor] for anchor in anchors]
node_feats[node] = np.array(feats)
return node_feats
def edge_centrality(net):
values ={}
bet = nx.edge_betweenness(net,normalized= True)
flow = nx.edge_current_flow_betweenness_centrality(net,normalized= True)
load = nx.edge_load(net)
com = nx.communicability(net)
bet_list =[]
flow_list = []
load_list = []
com_list = []
for edge,value in bet.iteritems() :
origin,end = edge
value_flow = max(flow.get(edge),flow.get((end,origin)))
values[edge] = [value,value_flow,load.get(edge),com.get(origin).get(end)]
bet_list.append(value)
flow_list.append(value_flow)
load_list.append(load.get(edge))
com_list.append(com.get(origin).get(end))
file3 = open("bl.csv",'w')
for xt in [bet_list,load_list,flow_list,com_list] :
for yt in [bet_list,load_list,flow_list,com_list] :
corr(xt,yt,file3)
print
file3.write("\n")
file3.close()
return values
def edge_centrality(net):
values = {}
bet = nx.edge_betweenness(net, normalized=True)
flow = nx.edge_current_flow_betweenness_centrality(net, normalized=True)
load = nx.edge_load(net)
com = nx.communicability(net)
bet_list = []
flow_list = []
load_list = []
com_list = []
for edge, value in bet.iteritems():
origin, end = edge
value_flow = max(flow.get(edge), flow.get((end, origin)))
values[edge] = [
value, value_flow,
load.get(edge),
com.get(origin).get(end)
]
bet_list.append(value)
flow_list.append(value_flow)
load_list.append(load.get(edge))
com_list.append(com.get(origin).get(end))
file3 = open("bl.csv", 'w')
for xt in [bet_list, load_list, flow_list, com_list]:
for yt in [bet_list, load_list, flow_list, com_list]:
corr(xt, yt, file3)
print
file3.write("\n")
file3.close()
return values
def metrics(self):
degree_centrality = nx.degree_centrality(self.g)
closeness_centrality = nx.closeness_centrality(self.g, distance=self.weight)
communicability = nx.communicability(self.g)
load_centrality = nx.load_centrality(self.g, weight=self.weight)
nodes_pagerank = nx.pagerank(self.g, weight=self.weight)
return degree_centrality, closeness_centrality, communicability, load_centrality, nodes_pagerank
def process_data(denom=100000, round=0):
f = csv.reader(open("../applab_new_6.csv", 'rb'), delimiter=',')
db = nx.DiGraph()
full_users = set()
i = 0
uniquect = 0
for line in f:
if i % 100000 == 0 : print "processed", i, "lines"
if i == 1000: break
sender, receiver, date, time, duration, cost, location, region = map(lambda x: x.strip(), line)
if sender not in full_users:
uniquect += 1
full_users.add(sender)
if uniquect <= 2: #% denom - round == 0:
db.add_node(sender)
if db.has_node(receiver) == False:
db.add_node(receiver)
else:
if db.has_node(receiver) == False:
db.add_node(receiver)
if db.has_edge(sender, receiver):
db[sender][receiver]['weight'] += int(duration)
else:
db.add_edge(sender, receiver, weight=int(duration))
i+=1
#pickle.dump(db, open("users_networkx.p" % str(round), "wb"))
#print "degree assortativity coeff:", nx.degree_assortativity_coefficient(db)
#print "average degree connectivity:", nx.average_degree_connectivity(db)
# print "k nearest neighbors:", nx.k_nearest_neighbors(db)
print "calculating deg cent"
deg_cent = nx.degree_centrality(db) #sorted(nx.degree_centrality(db).items(), key=lambda x: x[1])
print "calculating in deg cent"
in_deg_cent = nx.in_degree_centrality(db) #sorted(nx.in_degree_centrality(db).items(), key=lambda x: x[1])
print "calculating out deg cent"
out_deg_cent = nx.out_degree_centrality(db) #sorted(nx.out_degree_centrality(db).items(), key=lambda x: x[1])
print "closeness cent"
closeness_cent = nx.closeness_centrality(db) #sorted(nx.closeness_centrality(db).items(), key=lambda x: x[1])
#print "betweenness cent"
#btwn_cent = nx.betweenness_centrality(db) #sorted(nx.betweenness_centrality(db).items(), key=lambda x: x[1])
print "done"
w = open("../output/user_network_stats.csv", 'w')
w.write("uid,deg_cent,in_deg_cent,out_deg_cent,closeness_cent,btwn_cent\n")
for user in deg_cent.keys():
try:
w.write("%s,%s,%s,%s,%s\n" % (user, deg_cent[user], in_deg_cent[user], out_deg_cent[user], closeness_cent[user]))
except: pass
w.close()
print "drawing..."
nx.draw(db)
plt.savefig("path.pdf")
print "done!"
print "edge betweenness centrality:", nx.edge_betweenness_centrality(db)
print "communicability:", nx.communicability(db)
print "communicability centrality:", nx.communicability_centrality(db)
def compute_metric(self, metric='dijkstra'):
if metric == 'dijkstra':
print("computing the distance matrix on the graph")
self.distance = dict(nx.all_pairs_dijkstra_path_length(self.G))
else:
print("computing communicability..")
self.distance = nx.communicability(self.G)
print("done!")
return 1
def getSubGraphComm(network, this_TimeStamp = 0):
onGraph = getOnGraph(network, this_TimeStamp, False)
components = [i for i in nx.connected_components(onGraph)]
giant_component = components[np.argmax([len(i) for i in nx.connected_components(onGraph)])]
nodes = list(giant_component)
commMat = np.zeros((network.numOfWires, network.numOfWires))
subComm = nx.communicability(onGraph.subgraph(giant_component))
for i in nodes:
for j in nodes:
commMat[i,j] = subComm[i][j]
return commMat
def GetCommunicability(G, csvfile):
with open(csvfile, 'w') as file:
data = collections.OrderedDict()
nodes = sorted(nx.nodes(G))
comm = nx.communicability(G)
# Write Header
for n in nodes:
file.write(',' + str(n))
file.write('\n')
# Create and Write Body
for n in nodes:
s = str(n)
for k in nodes:
s += ','
if n != k:
s += str(comm[n][k])
file.write(s + '\n')
def set_capacities_edge_communicability(topology, capacities,
capacity_unit='Mbps'):
"""
Set link capacities proportionally to edge communicability centrality of
the link.
Parameters
----------
topology : Topology
The topology to which link capacities will be set
capacities : list
A list of all possible capacity values
capacity_unit : str, optional
The unit in which capacity value is expressed (e.g. Mbps, Gbps etc..)
"""
communicability = nx.communicability(topology)
centrality = {(u, v): communicability[u][v]
for (u, v) in topology.edges()}
_set_capacities_proportionally(topology, capacities, centrality,
capacity_unit=capacity_unit)
def set_capacities_edge_communicability(topology, capacities,
capacity_unit='Mbps'):
"""
Set link capacities proportionally to edge communicability centrality of
the link.
Parameters
----------
topology : Topology
The topology to which link capacities will be set
capacities : list
A list of all possible capacity values
capacity_unit : str, optional
The unit in which capacity value is expressed (e.g. Mbps, Gbps etc..)
"""
communicability = nx.communicability(topology)
centrality = {(u, v): communicability[u][v]
for (u, v) in topology.edges_iter()}
_set_capacities_proportionally(topology, capacities, centrality,
capacity_unit=capacity_unit)
def method1(df):
df['authors_parsed'] = df['authors_parsed'].apply(
lambda x: ast.literal_eval(x)[:3]) # 读取的str转list
all_authors = sum(df['authors_parsed'], [])
# 拼接所有的作者
authors_names = [' '.join(x[:-1]) for x in all_authors]
authors_names = pd.DataFrame(authors_names)
top10_names = authors_names[0].value_counts().index.values[:10].tolist()
print(top10_names)
G = nx.Graph()
n = 0
for i in range(len(df)):
authors = df.iloc[i]['authors_parsed']
authors = [' '.join(x[:-1]) for x in authors]
# print(authors)
if list(set(authors) & set(top10_names)):
n += 1
for author in authors[1:]:
G.add_edge(author[0], author)
print(n)
# 计算论文关系中有多少个联通子图
print(len(nx.communicability(G)))
nx.draw(G, with_labels=True)
degree_sequence = sorted([d for n, d in G.degree()], reverse=True)
dmax = max(degree_sequence)
plt.loglog(degree_sequence, "b-", marker="o")
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")
# draw graph in inset
plt.axes([0.45, 0.45, 0.45, 0.45])
Gcc = G.subgraph(
sorted(nx.connected_components(G), key=len, reverse=True)[0])
pos = nx.spring_layout(Gcc)
plt.axis("off")
nx.draw_networkx_nodes(Gcc, pos, node_size=20)
nx.draw_networkx_edges(Gcc, pos, alpha=0.4)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (1, 4), (4, 5)])
G2 = nx.generators.random_graphs.fast_gnp_random_graph(10, .3)
# nx.adjacency_matrix(G)
L = nx.laplacian(G) # L=D-A
# np.linalg.eigvals(L)
np.linalg.eig(L)
res = nx.laplacian_spectrum(G)
print res
print nx.normalized_laplacian(G)
c = nx.communicability(G)
# drawing
nx.draw(G) # default using spring_layout: force-directed
# same as:
# pos = nx.spring_layout(G)
# nx.draw(G, pos)
nx.draw_random(G)
nx.draw_spectral(G)
plt.show()
plt.savefig('path.png')
plt.cla()
# random graph
G = nx.generators.random_graphs.random_regular_graph(6, 50)
plt.show()
def get_comm(G):
GU = G.to_undirected()
return nx.communicability(GU)
def getCommMat(network):
adjMat = network.connectivity.adj_matrix
G = nx.from_numpy_array(adjMat)
comm = nx.communicability(G)
commMat = np.array([comm[i][j] for i in range(len(G)) for j in range(len(G))]).reshape(len(G),len(G))
return commMat
print(tree.edges(data=True)) # 最短路径 G = nx.path_graph(5) # 0-1-2-3-4链 print(nx.dijkstra_path(G, 0, 4)) # 所有节点之间的最短路径 G = nx.Graph() G.add_weighted_edges_from(g_data) gen = nx.all_pairs_shortest_path(G) print(dict(gen)) # 各点之间可达性 G = nx.Graph() G.add_weighted_edges_from(g_data) print(nx.communicability(G)) # 获得图中非连通点的列表 G = nx.Graph() G.add_edge(1, 2) G.add_node(3) print(list(nx.isolates(G))) # 遍历 G = nx.Graph() G.add_weighted_edges_from(g_data) d_gen = nx.dfs_edges(G, 1) # 按边深度搜索, 1为起点 b_gen = nx.bfs_edges(G, 1) print(list(d_gen), list(b_gen)) print(nx.dfs_tree(G, 1).nodes()) # 按点深搜
def centrality(self):
result = {}
result['degree_centrality'] = nx.degree_centrality(self.graph)
if self.directed == 'directed':
result['in_degree_centrality'] = nx.in_degree_centrality(
self.graph)
result['out_degree_centrality'] = nx.out_degree_centrality(
self.graph)
result['closeness_centrality'] = nx.closeness_centrality(self.graph)
result['betweenness_centrality'] = nx.betweenness_centrality(
self.graph)
# fix the tuple cant decode into json problem
stringify_temp = {}
temp = nx.edge_betweenness_centrality(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_betweenness_centrality'] = stringify_temp
if self.directed == 'undirected':
result[
'current_flow_closeness_centrality'] = nx.current_flow_closeness_centrality(
self.graph)
result[
'current_flow_betweenness_centrality'] = nx.current_flow_betweenness_centrality(
self.graph)
stringify_temp = {}
temp = nx.edge_current_flow_betweenness_centrality(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_current_flow_betweenness_centrality'] = stringify_temp
result[
'approximate_current_flow_betweenness_centrality'] = nx.approximate_current_flow_betweenness_centrality(
self.graph)
result['eigenvector_centrality'] = nx.eigenvector_centrality(
self.graph)
result[
'eigenvector_centrality_numpy'] = nx.eigenvector_centrality_numpy(
self.graph)
result['katz_centrality'] = nx.katz_centrality(self.graph)
result['katz_centrality_numpy'] = nx.katz_centrality_numpy(
self.graph)
result['communicability'] = nx.communicability(self.graph)
result['communicability_exp'] = nx.communicability_exp(self.graph)
result[
'communicability_centrality'] = nx.communicability_centrality(
self.graph)
result[
'communicability_centrality_exp'] = nx.communicability_centrality_exp(
self.graph)
result[
'communicability_betweenness_centrality'] = nx.communicability_betweenness_centrality(
self.graph)
result['estrada_index'] = nx.estrada_index(self.graph)
result['load_centrality'] = nx.load_centrality(self.graph)
stringify_temp = {}
temp = nx.edge_load(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_load'] = stringify_temp
result['dispersion'] = nx.dispersion(self.graph)
fname_centra = self.DIR + '/centrality.json'
with open(fname_centra, "w") as f:
json.dump(result, f, cls=SetEncoder, indent=2)
print(fname_centra)
def function(input):
if input == 1:
clustering_coefficient = nx.clustering(G)
clustering_coefficient = normalise(clustering_coefficient)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
clustering_coefficient.keys())
train_data, dev_data, test_data = write_train_test_dev(
clustering_coefficient, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_clustering_coefficient.txt")
write_to_file(dev_data, baseDir + "dev_clustering_coefficient.txt")
write_to_file(test_data, baseDir + "test_clustering_coefficient.txt")
if input == 2:
betweenness_centrality = nx.betweenness_centrality(G, normalized=True)
betweenness_centrality = normalise(betweenness_centrality)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
betweenness_centrality.keys())
train_data, dev_data, test_data = write_train_test_dev(
betweenness_centrality, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_betweenness_centrality.txt")
write_to_file(dev_data, baseDir + "dev_betweenness_centrality.txt")
write_to_file(test_data, baseDir + "test_betweenness_centrality.txt")
if input == 3:
closeness_centrality = nx.closeness_centrality(G, normalized=True)
closeness_centrality = normalise(closeness_centrality)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
closeness_centrality.keys())
train_data, dev_data, test_data = write_train_test_dev(
closeness_centrality, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_closeness_centrality.txt")
write_to_file(dev_data, baseDir + "dev_closeness_centrality.txt")
write_to_file(test_data, baseDir + "test_closeness_centrality.txt")
if input == 4:
average_neighbor_degree = nx.average_neighbor_degree(G)
average_neighbor_degree = normalise(average_neighbor_degree)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
average_neighbor_degree.keys())
train_data, dev_data, test_data = write_train_test_dev(
average_neighbor_degree, train_keys, dev_keys, test_keys)
write_to_file(train_data,
baseDir + "train_average_neighbor_degree.txt")
write_to_file(dev_data, baseDir + "dev_average_neighbor_degree.txt")
write_to_file(test_data, baseDir + "test_average_neighbor_degree.txt")
if input == 5:
degree_centrality = nx.degree_centrality(G)
degree_centrality = normalise(degree_centrality)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
degree_centrality.keys())
train_data, dev_data, test_data = write_train_test_dev(
degree_centrality, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_degree_centrality.txt")
write_to_file(dev_data, baseDir + "dev_degree_centrality.txt")
write_to_file(test_data, baseDir + "test_degree_centrality.txt")
if input == 6:
load_centrality = nx.load_centrality(G, normalized=True)
load_centrality = normalise(load_centrality)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
load_centrality.keys())
train_data, dev_data, test_data = write_train_test_dev(
load_centrality, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_load_centrality.txt")
write_to_file(dev_data, baseDir + "dev_load_centrality.txt")
write_to_file(test_data, baseDir + "test_load_centrality.txt")
if input == 7:
shortest_path_length_dict = nx.shortest_path_length(G)
shortest_path_length = {}
for key_1 in shortest_path_length_dict:
for key_2 in shortest_path_length_dict[key_1]:
shortest_path_length[
str(key_1) + "\t" +
str(key_2)] = shortest_path_length_dict[key_1][key_2]
shortest_patth_length = normalise(shortest_path_length)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
shortest_path_length.keys())
train_data, dev_data, test_data = write_train_test_dev(
shortest_path_length, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_shortest_path_length.txt")
write_to_file(dev_data, baseDir + "dev_shortest_path_length.txt")
write_to_file(test_data, baseDir + "test_shortest_path_length.txt")
if input == 8:
jaccard_coefficient = nx.jaccard_coefficient(G)
jaccard_coefficient_dict = {}
for u, v, p in jaccard_coefficient:
jaccard_coefficient_dict[str(u) + "\t" + str(v)] = p
jaccard_coefficient_dict = normalise(jaccard_coefficient_dict)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
jaccard_coefficient_dict.keys())
train_data, dev_data, test_data = write_train_test_dev(
jaccard_coefficient_dict, train_keys, dev_keys, test_keys)
write_to_file(train_data,
baseDir + "train_jaccard_coefficient_dict.txt")
write_to_file(dev_data, baseDir + "dev_jaccard_coefficient_dict.txt")
write_to_file(test_data, baseDir + "test_jaccard_coefficient_dict.txt")
if input == 9:
katz_centrality = nx.katz_centrality(G)
katz_centrality = normalise(katz_centrality)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
katz_centrality.keys())
train_data, dev_data, test_data = write_train_test_dev(
katz_centrality, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_katz_centrality.txt")
write_to_file(dev_data, baseDir + "dev_katz_centrality.txt")
write_to_file(test_data, baseDir + "test_katz_centrality.txt")
if input == 10:
pagerank = nx.pagerank(G)
pagerank = normalise(pagerank)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
pagerank.keys())
train_data, dev_data, test_data = write_train_test_dev(
pagerank, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_pagerank.txt")
write_to_file(dev_data, baseDir + "dev_pagerank.txt")
write_to_file(test_data, baseDir + "test_pagerank.txt")
if input == 11:
communicability = nx.communicability(G)
communicability = normalise(pagerank)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
communicability.keys())
train_data, dev_data, test_data = write_train_test_dev(
communicability, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_communicability.txt")
write_to_file(dev_data, baseDir + "dev_communicability.txt")
write_to_file(test_data, baseDir + "test_communicability.txt")
if input == 12:
degree = G.degree()
degree = normalise(degree)
train_keys, dev_keys, test_keys = create_train_test_dev_split(
degree.keys())
train_data, dev_data, test_data = write_train_test_dev(
degree, train_keys, dev_keys, test_keys)
write_to_file(train_data, baseDir + "train_degree.txt")
write_to_file(dev_data, baseDir + "dev_degree.txt")
write_to_file(test_data, baseDir + "test_degree.txt")
def features_part2(info):
"""
third set of features.
"""
G = info['G']
n = info['num_nodes']
num_units = info['num_units']
edges = info['edges']
nedges = len(edges)
H = G.to_undirected()
res = dict()
cc = nx.closeness_centrality(G)
res['closeness_centrality'] = cc[n - 1]
res['closeness_centrality_mean'] = np.mean(list(cc.values()))
bc = nx.betweenness_centrality(G)
res['betweenness_centrality_mean'] = np.mean(list(bc.values()))
cfcc = nx.current_flow_closeness_centrality(H)
res['current_flow_closeness_centrality_mean'] = np.mean(list(
cfcc.values()))
cfbc = nx.current_flow_betweenness_centrality(H)
res['current_flow_betweenness_centrality_mean'] = np.mean(
list(cfbc.values()))
soc = nx.second_order_centrality(H)
res['second_order_centrality_mean'] = np.mean(list(soc.values())) / n
cbc = nx.communicability_betweenness_centrality(H)
res['communicability_betweenness_centrality_mean'] = np.mean(
list(cbc.values()))
comm = nx.communicability(H)
res['communicability'] = np.log(comm[0][n - 1])
res['communicability_start_mean'] = np.log(np.mean(list(comm[0].values())))
res['communicability_end_mean'] = np.log(
np.mean(list(comm[n - 1].values())))
res['radius'] = nx.radius(H)
res['diameter'] = nx.diameter(H)
res['local_efficiency'] = nx.local_efficiency(H)
res['global_efficiency'] = nx.global_efficiency(H)
res['efficiency'] = nx.efficiency(H, 0, n - 1)
pgr = nx.pagerank_numpy(G)
res['page_rank'] = pgr[n - 1]
res['page_rank_mean'] = np.mean(list(pgr.values()))
cnstr = nx.constraint(G)
res['constraint_mean'] = np.mean(list(cnstr.values())[:-1])
effsize = nx.effective_size(G)
res['effective_size_mean'] = np.mean(list(effsize.values())[:-1])
cv = np.array(list(nx.closeness_vitality(H).values()))
cv[cv < 0] = 0
res['closeness_vitality_mean'] = np.mean(cv) / n
res['wiener_index'] = nx.wiener_index(H) / (n * (n - 1) / 2)
A = nx.to_numpy_array(G)
expA = expm(A)
res['expA'] = np.log(expA[0, n - 1])
res['expA_mean'] = np.log(np.mean(expA[np.triu_indices(n)]))
return res
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (1, 4), (4, 5)])
G2 = nx.generators.random_graphs.fast_gnp_random_graph(10, .3)
# nx.adjacency_matrix(G)
L = nx.laplacian(G) # L=D-A
# np.linalg.eigvals(L)
np.linalg.eig(L)
res = nx.laplacian_spectrum(G)
print res
print nx.normalized_laplacian(G)
c = nx.communicability(G)
# drawing
nx.draw(G) # default using spring_layout: force-directed
# same as:
# pos = nx.spring_layout(G)
# nx.draw(G, pos)
nx.draw_random(G)
nx.draw_spectral(G)
plt.show()
plt.savefig('path.png')
plt.cla()
# random graph
G = nx.generators.random_graphs.random_regular_graph(6, 50)
plt.show()
