def get_layer_info(subject, journal_volume, edge_list):
G = nx.Graph()
G.add_weighted_edges_from(edge_list)
PATH = "C:/Users/hexie/Documents/APS_result/" + str(
journal_volume) + "/" + str(subject)
try:
os.mkdir(PATH)
os.chdir(PATH)
except:
os.chdir(PATH)
degree_centrality = nxc.degree_centrality(G)
try:
eigen_vector_centrality = nxc.eigenvector_centrality(G)
np.save("eigen_vector_centrality.npy", eigen_vector_centrality)
except:
print("fail to converge within 100 iterations of power")
closeness_centrality = nxc.closeness_centrality(G)
betweeness_centrality = nxc.betweenness_centrality(G)
np.save("degree_centrality.npy", degree_centrality)
np.save("closeness_centrality.npy", closeness_centrality)
np.save("betweeness_centrality.npy", betweeness_centrality)
with open(str(subject) + str(journal_volume) + ".txt", 'w') as f:
f.write('Number of Edges: ' + str(nx.number_of_edges(G)) + "\n")
f.write('Number of Nodes: ' + str(nx.number_of_nodes(G)) + "\n")
nx.draw(G)
plt.savefig(str(subject) + str(journal_volume) + ".png")
plt.clf()
def get_centrality(x, edge_index, batch):
num_graphs = batch[-1] + 1
N = x.shape[0]
num_nodes = scatter_add(batch.new_ones(x.size(0)), batch, dim=0)
cum_num_nodes = torch.cat(
[num_nodes.new_zeros(1),
num_nodes.cumsum(dim=0)[:-1]], dim=0)
cum_num_nodes = torch.cat((cum_num_nodes, torch.tensor([N]).cuda()))
row, col = edge_index
c_centrality = []
d_centrality = []
for i in range(num_graphs):
'''each graph'''
s_id = cum_num_nodes[i]
e_id = cum_num_nodes[i + 1]
mask = torch.eq(row, s_id)
for node in range(s_id + 1, e_id):
mask = mask + torch.eq(row, node)
g_row = torch.masked_select(row, mask) - s_id
g_col = torch.masked_select(col, mask) - s_id
G = to_networkx(torch.stack([g_row, g_col], dim=0))
c_centrality = c_centrality + list(closeness_centrality(G).values())
d_centrality = d_centrality + list(degree_centrality(G).values())
c_centrality = torch.Tensor(c_centrality).cuda()
d_centrality = torch.Tensor(d_centrality).cuda()
return c_centrality, d_centrality
def get_topological_features(G, nodes=None):
N_ = len(G.nodes)
if nodes is None:
nodes = G.nodes
# Degree centrality
d_c = get_features(degree_centrality(G).values())
print 'a'
# Betweeness centrality
b_c = get_features(betweenness_centrality(G).values())
print 'b'
# Close ness centrality
c_c = get_features(closeness_centrality(G).values())
print 'c'
# Clustering
c = get_features(clustering(G).values())
print 'd'
d = diameter(G)
r = radius(G)
s_p_average = []
for s in shortest_path_length(G):
dic = s[1]
lengths = dic.values()
s_p_average += [sum(lengths) / float(N_)]
s_p_average = get_features(s_p_average)
features = np.concatenate((d_c, b_c, c_c, c, s_p_average, [d], [r]),
axis=0)
return features
def get_centrality_labels(knn_graph_obj, perc_labeled, type='degree'):
import random
if type == 'degree':
degree_centrality_knn = pd.DataFrame.from_dict(centrality.degree_centrality(knn_graph_obj), orient='index', columns=['value'])
node_toget_labels = degree_centrality_knn.sort_values(by = 'value', ascending = False).index[0:int(perc_labeled*len(degree_centrality_knn.index))].tolist()
elif type == 'closeness':
closeness_centrality_knn = pd.DataFrame.from_dict(centrality.closeness_centrality(knn_graph_obj), orient='index', columns=['value'])
node_toget_labels = closeness_centrality_knn.sort_values(by = 'value', ascending = False).index[0:int(perc_labeled*len(closeness_centrality_knn.index))].tolist()
elif type == 'betweenness':
betweenness_centrality_knn = pd.DataFrame.from_dict(centrality.betweenness_centrality(knn_graph_obj), orient='index', columns=['value'])
node_toget_labels = betweenness_centrality_knn.sort_values(by = 'value', ascending = False).index[0:int(perc_labeled*len(betweenness_centrality_knn.index))].tolist()
elif type == 'katz':
katz_centrality_knn = pd.DataFrame.from_dict(centrality.katz_centrality(knn_graph_obj), orient='index', columns=['value'])
node_toget_labels = katz_centrality_knn.sort_values(by = 'value', ascending = False).index[0:int(perc_labeled*len(katz_centrality_knn.index))].tolist()
elif type == 'clustering':
clustering_knn = pd.DataFrame.from_dict(clustering(knn_graph_obj), orient='index', columns=['value'])
node_toget_labels = clustering_knn.sort_values(by = 'value', ascending = False).index[0:int(perc_labeled*len(clustering_knn.index))].tolist()
else:
indexes = list(knn_graph_obj.nodes)
#print(indexes)
node_toget_labels = random.sample(indexes, int(perc_labeled*len(indexes)))
#print(node_toget_labels)
return node_toget_labels
def get_centrality_labels(knn_graph_obj, type='degree'):
import random
if type == 'degree':
node_toget_labels = pd.DataFrame.from_dict(
centrality.degree_centrality(knn_graph_obj),
orient='index',
columns=['value'])
elif type == 'closeness':
node_toget_labels = pd.DataFrame.from_dict(
centrality.closeness_centrality(knn_graph_obj),
orient='index',
columns=['value'])
elif type == 'betweenness':
node_toget_labels = pd.DataFrame.from_dict(
centrality.betweenness_centrality(knn_graph_obj),
orient='index',
columns=['value'])
elif type == 'clustering':
node_toget_labels = pd.DataFrame.from_dict(clustering(knn_graph_obj),
orient='index',
columns=['value'])
else:
node_toget_labels = list(knn_graph_obj.nodes)
#print(node_toget_labels)
return node_toget_labels
def extract_closeness_centrality(self):
output = open(
'output/' + self.set_ + '/' + self.set_ +
'_closeness_centrality.csv', 'w')
print('Calculating closeness centrality')
nodes = centrality.closeness_centrality(self.G)
for key in nodes:
output.write(str(key) + ',' + str(nodes[key]) + '\n')
def get_closeness_centrality(dataset):
centrality = []
for data in dataset:
'''each graph'''
g_row, g_col = data.edge_index
G = to_networkx(torch.stack([g_row, g_col], dim=0))
c_centrality = list(closeness_centrality(G).values())
centrality = centrality + c_centrality
return centrality
def get_centrality(def_centrality, toll_centrality, model, nodes_int):
left_b_centr = centrality.betweenness_centrality(model.get_nx_graph())
left_c_centr = centrality.closeness_centrality(model.get_nx_graph())
for el in left_b_centr:
node = model.get_node_by_id(el)
if node in nodes_int:
toll_centrality += left_c_centr[el] + left_b_centr[el]
else:
def_centrality += left_c_centr[el] + left_b_centr[el]
return def_centrality, toll_centrality
def compute_metrics(graph):
G = json_graph.node_link_graph(graph, multigraph=False)
degree_centrality = centrality.degree_centrality(G)
closeness_centrality = centrality.closeness_centrality(G)
betweenness_centrality = centrality.betweenness_centrality(G)
page_rank = link_analysis.pagerank_alg.pagerank(G)
max_clique = approximation.clique.max_clique(G)
diameters = [distance_measures.diameter(g) for g in connected_component_subgraphs(G)]
copy = dict()
copy['id'] = graph['id']
copy['name'] = graph['name']
copy['graph'] = dict()
copy['graph']['nodes'] = graph['nodes']
copy['graph']['links'] = graph['links']
copy['metrics'] = dict()
# diameters
copy['metrics']['diameter'] = dict()
copy['metrics']['diameter']['all'] = diameters
copy['metrics']['diameter']['max'] = max(diameters)
copy['metrics']['diameter']['average'] = float(sum(diameters)) / float(len(diameters))
# clique size
copy['metrics']['maxClique'] = len(list(max_clique))
# degree centrality
copy['metrics']['degreeCentrality'] = dict()
copy['metrics']['degreeCentrality']['byId'] = degree_centrality
copy['metrics']['degreeCentrality']['max'] = sum(degree_centrality.values())
copy['metrics']['degreeCentrality']['average'] = float(sum(degree_centrality.values())) / float(len(degree_centrality.values()))
# closeness centrality
copy['metrics']['closenessCentrality'] = dict()
copy['metrics']['closenessCentrality']['byId'] = closeness_centrality
copy['metrics']['closenessCentrality']['max'] = sum(closeness_centrality.values())
copy['metrics']['closenessCentrality']['average'] = float(sum(closeness_centrality.values())) / float(len(closeness_centrality.values()))
# degree centrality
copy['metrics']['betweennessCentrality'] = dict()
copy['metrics']['betweennessCentrality']['byId'] = betweenness_centrality
copy['metrics']['betweennessCentrality']['max'] = sum(betweenness_centrality.values())
copy['metrics']['betweennessCentrality']['average'] = float(sum(betweenness_centrality.values())) / float(len(betweenness_centrality.values()))
# degree centrality
copy['metrics']['pageRank'] = dict()
copy['metrics']['pageRank']['byId'] = page_rank
copy['metrics']['pageRank']['max'] = sum(page_rank.values())
copy['metrics']['pageRank']['average'] = float(sum(page_rank.values())) / float(len(page_rank.values()))
return copy
def getClosenessCentrality(G, normalized):
closeness_centrality = centrality.closeness_centrality(G)
if not normalized:
return closeness_centrality
max_closeness = closeness_centrality[max(closeness_centrality,
key=closeness_centrality.get)]
normalize(closeness_centrality, max_closeness)
print(closeness_centrality)
color(G, closeness_centrality, 1, "olivedrab")
return closeness_centrality
def get_closeness_centrality(G, **kwargs):
"""Returns a dictionary of closeness centrality
values for all nodes.
"""
# Get the graph without glycine residues
H = get_graph_without_glycine(G, kwargs["identifiers"],
kwargs["residue_names"])
# Calculate the closeness centrality values
centrality_dict = \
nxc.closeness_centrality(G = G,
distance = kwargs["weight"])
# Return the finalized the dictionary of centrality values
return finalize_dict(G, centrality_dict)
def centralization_metrics(G, prefix=""):
# NB: G can be either directed or undirected network
# Metrics:
# (betweennes / closeness / eigenvector / pagerank)
# betweenness
# => expensive
# sample: k=min(10, len(G))
betweenness = betweenness_centrality(G, normalized=True)
betweenness_arr = np.fromiter(betweenness.values(), dtype=np.float)
betweenness_mean = np.mean(np.max(betweenness_arr) - betweenness_arr)
# closeness
# => expensive
# NB: normilizes by the CC size
closeness = closeness_centrality(G, wf_improved=False)
closeness_arr = np.fromiter(closeness.values(), dtype=np.float)
closeness_mean = np.mean(np.max(closeness_arr) - closeness_arr)
# eigenvector
eigenvec_mean = None
if len(G) > 2:
try:
eigenvec = eigenvector_centrality_numpy(G)
eigenvec_arr = np.fromiter(eigenvec.values(), dtype=np.float)
eigenvec_mean = np.mean(np.max(eigenvec_arr) - eigenvec_arr)
except:
eigenvec_mean = None
# pagerank
try:
pagerank = pagerank_numpy(G)
pagerank_arr = np.fromiter(pagerank.values(), dtype=np.float)
pagerank_mean = np.mean(np.max(pagerank_arr) - pagerank_arr)
except:
pagerank_mean = None
centralization = {
f"cent{prefix}_betweenness_mean": betweenness_mean,
f"cent{prefix}_closeness_mean": closeness_mean,
f"cent{prefix}_eigenvec_mean": eigenvec_mean,
f"cent{prefix}_pagerank_mean": pagerank_mean
}
return centralization
def calc_node_based_centrality(edge_index, centrality='degree'):
adj_list = edge_index.numpy().T
G = nx.Graph()
G.add_edges_from(adj_list)
if centrality == 'degree':
nodes_centrality = degree_centrality(G)
elif centrality == 'eigenvector':
nodes_centrality = eigenvector_centrality(G)
elif centrality == "closeness":
nodes_centrality = closeness_centrality(G)
else:
print(centrality, "is not defined")
exit(1)
edges_centrality = dict()
for u, v in adj_list:
edges_centrality[(u, v)] = nodes_centrality[u] * nodes_centrality[v]
return edges_centrality
def top_nodes(G, k=3):
"""
Returns the top k nodes for various
centrality measures: degree,
betweennes and closeness.
Args:
G (nx.Graph): graph for which the
top nodes must be determined.
k (int): number of top nodes to return.
if set to -ve, all the nodes will be
returned.
Returns:
res_dict (dict): dictionary of each centrality
measure with list of top k nodes in that
measure as values to the dictionary.
"""
# number of nodes in the graph each node is connected to
node_deg_dict = centrality.degree_centrality(G)
# number of all pair shortest paths that pass through each node
node_btw_dict = centrality.betweenness_centrality(G)
# number of neighbours connected to each other for each node
node_clo_dict = centrality.closeness_centrality(G)
# sort by nodes by each centrality measure in decreasing order
top_k_deg_nodes = sorted(node_deg_dict.items(), key=lambda x: -x[1])
top_k_btw_nodes = sorted(node_btw_dict.items(), key=lambda x: -x[1])
top_k_clo_nodes = sorted(node_clo_dict.items(), key=lambda x: -x[1])
# pick the top k nodes
res_dict = dict()
if k > 0:
res_dict["degree"] = list(zip(*top_k_deg_nodes[:k]))[0]
res_dict["betweenness"] = list(zip(*top_k_btw_nodes[:k]))[0]
res_dict["closeness"] = list(zip(*top_k_clo_nodes[:k]))[0]
else:
res_dict["degree"] = list(zip(*top_k_deg_nodes))[0]
res_dict["betweenness"] = list(zip(*top_k_btw_nodes))[0]
res_dict["closeness"] = list(zip(*top_k_clo_nodes))[0]
return res_dict
def parse(name):
print(name)
pathbase = path.abspath(path.dirname(__file__))
G = nx.Graph()
data = json.load(open('{0}/{1}.json'.format(pathbase, name)))
nodes = data['nodes']
text = {i: node['text'] for i, node in enumerate(nodes)}
weight = {i: float(node['weight']) for i, node in enumerate(nodes)}
for i in range(len(nodes)):
G.add_node(i)
for link in data['links']:
G.add_edge(link['source'], link['target'])
degree = centrality.degree_centrality(G)
closeness = centrality.closeness_centrality(G)
betweenness = centrality.betweenness_centrality(G)
#edge_betweenness = centrality.edge_betweenness_centrality(G)
#current_flow_closeness = centrality.current_flow_closeness_centrality(G)
#current_flow_betweenness =\
# centrality.current_flow_betweenness_centrality(G)
try:
eigenvector = centrality.eigenvector_centrality(G, max_iter=1000)
except:
eigenvector = {i: 0 for i in range(len(nodes))}
katz = centrality.katz_centrality(G)
obj = {'nodes': [], 'links': data['links']}
for i in range(len(nodes)):
obj['nodes'].append({
'text': text[i],
'weight': weight[i],
'degree': degree[i],
'closeness': closeness[i],
'betweenness': betweenness[i],
#'edge_betweenness': edge_betweenness[i],
#'current_flow_closeness': current_flow_closeness[i],
#'current_flow_betweenness': current_flow_betweenness[i],
'eigenvector': eigenvector[i],
'katz': katz[i],
})
json.dump(obj,
open('{0}/../data/{1}.json'.format(pathbase, name), 'w'),
sort_keys=True)
def calculate_metrics(network):
'''
Calcula las métricas más importantes sobre la red y las devuelve en forma de diccionario
Parameters
----------
network : nx.Graph
Red de la que se quiere calcular las metricas
Returns
-------
metrics : dict
Diccionario que almacena las métricas para la red pasada por parámetro
'''
# Incialización del diccionario que almacena las métricas
metrics = {}
# Obtenemos los nombres y afiliaciones de la red
names = nx.get_node_attributes(network, 'name')
affiliation = nx.get_node_attributes(network, 'affiliation')
# Función para obtener el nombre en una tupla
getprops = lambda author_tuple: (names[author_tuple[0]], affiliation[
author_tuple[0]], author_tuple[1])
# Número de nodos y aristas
n = len(network.nodes.data())
m = len(network.edges.data())
metrics['n'] = n
metrics['m'] = m
# Tamaño total de la red (suma de pesos)
metrics['size'] = network.size(weight='weight')
# Grado promedio, densidad y grado máximo
metrics['av_degree'] = round((2 * m) / n, 5)
metrics['density'] = (2 * m) / (n * (n - 1))
metrics['max_degree'] = getprops(
max(dict(network.degree()).items(), key=lambda degree: degree[1]))[0]
# Distribución de probabilidad del grado
degree_distribution = [
(i,
len([author
for (author, degree) in network.degree() if degree == i]) /
len(network.nodes.data())) for i in range(
max(dict(network.degree()).items(), key=lambda degree: degree[1])
[1])
]
metrics['max_degree_p'] = max(degree_distribution,
key=lambda degree_p: degree_p[1])[0]
# Coeficiente de clustering promedio
metrics['clustering_coefficient'] = average_clustering(network)
# Nodo con mayor centralidad promedio
metrics['max_closeness_centrality'] = getprops(
max(centrality.closeness_centrality(network).items(),
key=lambda pair: pair[1]))[0]
return metrics
def std_closeness_centrality(self, graph):
close_centr = closeness_centrality(graph)
return np.std(list(close_centr.values()))
def avg_closeness_centrality(self, graph):
close_centr = closeness_centrality(graph)
return np.average(list(close_centr.values()))
return G
def draw_graph(G):
nx.draw(G, node_size=30)
plt.show()
if __name__ == "__main__":
print("Start parsing:")
data = parse_group()
G = create_graph(data)
draw_graph(G)
degree = pd.Series(nxa.degree_centrality(G)).idxmax()
closeness = pd.Series(nxa.closeness_centrality(G)).idxmax()
eigenvector = pd.Series(nxa.eigenvector_centrality(G)).idxmax()
betweennes = pd.Series(nxa.betweenness_centrality(G)).idxmax()
degree_user = api.users.get(user_ids=degree)[0]
closeness_user = api.users.get(user_ids=closeness)[0]
eigenvector_user = api.users.get(user_ids=eigenvector)[0]
betweeness_user = api.users.get(user_ids=betweennes)[0]
print("Most important user:"******"Degree centrality: id{degree} - {degree_user['first_name'] + ' ' + degree_user['last_name']}"
)
print(
f"Closeness centrality: id{closeness} - {closeness_user['first_name'] + ' ' + closeness_user['last_name']}"
)
g_dict[i] = i
c_list = []
for j in range(len(d)):
for n in range(len(c)):
if d[j] in c[n]:
c_list.append(g_dict[n])
#中心性解析
#次数中心性
cent_values = degree_centrality(G).values()
cent_central = degree_centrality(G)
cent_keys = degree_centrality(G).keys()
#近接中心性
d_values = closeness_centrality(G).values()
d_central = closeness_centrality(G)
d_keys = closeness_centrality(G).keys()
#媒介中心性
bet_values = betweenness_centrality(G).values()
bet_central = betweenness_centrality(G)
bet_keys = betweenness_centrality(G).keys()
#sorted(d_dict.values())
d_values_list = list(d_values)
#print(d_values)
#print(d_keys)
#print(d_values_list)
a = 0
b = 0
G = read_dot(os.path.join("output", fname))
nx.draw(G)
except:
print("cannot load graph")
continue
if G.number_of_nodes() == 0:
print("Cannot read binary file")
continue
data = []
data.append(fname)
data.append(G.number_of_nodes())
data.append(G.number_of_edges())
data.append(density(G))
deg_centrality = degree_centrality(G)
data.extend(properties_of_array(deg_centrality))
cln_centrality = closeness_centrality(G)
data.extend(properties_of_array(cln_centrality))
btn_centrality = betweenness_centrality(G)
data.extend(properties_of_array(btn_centrality))
st_path = shortest_path(G)
deg = [len(val) for key, val in st_path.items()]
d = np.array(deg)
data.extend(
[np.min(d),
np.max(d),
np.median(d),
np.mean(d),
np.std(d)])
try:
data.append(diameter(G.to_undirected()))
except:
def summarise_communities(relG):
"""
Creates summaries for all clusters
in a given graph with the following
attributes:
1. bridges - node with the highest betweeness
2. members
3. Closeness - how close the group (higher, closer)
4. relation counts in the cluster
Args:
relG (nx.Graph): graph for which
the summary must be created.
Note: the edges in relG must have
'relation' edge attribute referring
to the relation type.
Returns:
summaries (dict): dictionary of clusters
with the string summaries as values.
"""
# get communities
communities = list(enumerate(detect_communities(relG)))
node_comm_dict = dict()
for i, c in communities:
for u in c:
node_comm_dict[u] = i
# get bridges: nodes in clusters in highest betweenness centrality
node_btw_dict = centrality.betweenness_centrality(relG)
comm_bridge_dict = {i: (None, -1) for i in range(len(communities))}
for n, b in node_btw_dict.items():
c = node_comm_dict[n]
if b > comm_bridge_dict[c][1]:
comm_bridge_dict[c] = (n, b)
# get powers of clusters
comm_graph_dict = {i: nx.Graph() for i, c in communities}
for (u, v, r) in relG.edges.data('relation'):
uc, vc = node_comm_dict[u], node_comm_dict[v]
if uc == vc:
comm_graph_dict[uc].add_edge(u, v, relation=r)
# get average closeness centrality
comm_avg_clo_dict = dict()
for i, G in comm_graph_dict.items():
node_clo_dict = centrality.closeness_centrality(G)
s, n = sum(list(node_clo_dict.values())), len(node_clo_dict)
comm_avg_clo_dict[i] = s / n
# get relation counts for each cluster
rel_count_comm_dict = dict()
for i, c in comm_graph_dict.items():
u, v, r = list(zip(*c.edges.data('relation')))
rels, counts = np.unique(r, return_counts=True)
rel_count_comm_dict[i] = list(zip(rels, counts))
# create string summary for each cluster
summaries = dict()
for i, c in communities:
summaries[i] = f"Bridge: {comm_bridge_dict[i][0]}\n"
members = ", ".join([str(x) for x in c])
summaries[i] += f"Members: {members}\n"
closeness = np.around(comm_avg_clo_dict[i], decimals=4)
summaries[i] += f"Closeness: {closeness}\n"
rel_counts = [
'\'' + r + '\'- ' + str(c) for r, c in rel_count_comm_dict[i]
]
summaries[i] += "Relations:\n" + "\n".join(rel_counts)
return summaries
def closenesscentrality(self, brain,outfilebase = "brain", append=True):
""" Calculates node and hub closeness centralities. For hub centralities there are two files, one with the values in and another
with the hub identities in corresponding rows.
"""
## closeness centrality
# node centrality
outfile = outfilebase+'_closeness_centralities_nodes'
boolVal = self.fileCheck(outfile, append)
# open file and write headers if necessary
if append and boolVal:
f= open(outfile,"ab")
headers = None
else:
f = open(outfile,"wb")
headers = dict((n,n) for n in brain.G.nodes())
writeObj = DictWriter(f,fieldnames = brain.G.nodes())
if headers:
writeObj.writerow(headers)
# make calculations
centralities = centrality.closeness_centrality(brain.G) # calculate centralities for largest connected component
nodecentralitiestowrite = dict((n,None) for n in brain.G.nodes()) # create a blank dictionary of all nodes in the graph
for node in centralities:
nodecentralitiestowrite[node] = centralities[node] # populate the blank dictionary with centrality values
writeObj.writerow(nodecentralitiestowrite) # write out centrality values
f.close()
# hub centrality
outfile = outfilebase+'_closeness_centralities_hubs'
hubidfile = outfilebase+'_closeness_centralities_hubs_ids'
OFbool = self.fileCheck(outfile, append)
self.fileCheck(hubidfile, append)
if append and OFbool:
f = open(outfile,"ab")
g = open(hubidfile,"ab")
else:
f= open(outfile,"wb")
g = open(hubidfile,"wb")
centhubs = [hub for hub in brain.hubs if hub in brain.G] # hubs within largest connected graph component
# write hub identifies to file
writeObj = DictWriter(f,fieldnames = brain.hubs)
hubwriter = DictWriter(g,fieldnames = brain.hubs)
headers = dict((n,n) for n in brain.hubs) # dictionary of all hubs in network to write
hubwriter.writerow(headers)
hubcentralitieistowrite = dict((n,None) for n in brain.hubs) # empty dictionary to populate with centralities data
for hub in centhubs:
hubcentralitieistowrite[hub] = nodecentralitiestowrite[hub]
writeObj.writerow(hubcentralitieistowrite)
f.close()
g.close()
class Create_network():
centrality_metrics = {
"Degree Centrality":
lambda n, weight='_': centrality_algorithms.degree_centrality(n),
"Closeness Centrality":
lambda n, weight='_': centrality_algorithms.closeness_centrality(n),
"Betweenness Centrality":
centrality_algorithms.betweenness_centrality
}
community_colors = ['#e41a1c','#377eb8','#4daf4a','#984ea3','#ff7f00','#ffff33','#a65628', \
'#b3cde3','#ccebc5','#decbe4','#fed9a6','#ffffcc','#e5d8bd','#fddaec',\
'#1b9e77','#d95f02','#7570b3','#e7298a','#66a61e','#e6ab02','#a6761d','#666666']
def __init__(self,
network_file,
layout_file,
count_path,
title,
width=800,
thresh_val=8):
self.network_file = network_file
self.layout_file = layout_file
self.count_path = count_path
self.network_tuple = self.load_network(network_file, layout_file)
self.nodes_sources_tab1 = self.column_source(self.network_tuple[1],
count_path)
self.network_plots_n_circle_tab1 = self.create_network_plot(
self.nodes_sources_tab1, title, width)
self.network_lines_tab1 = self.add_lines(
self.network_tuple, self.network_plots_n_circle_tab1[0])
self.get_centrality_n_community(self.network_tuple[0],
self.nodes_sources_tab1,
self.network_plots_n_circle_tab1[1])
self.drop_button_tab1 = Button(label="Remove Node",
button_type="warning")
self.drop_button_tab1.on_click(self.remove_node_tab1)
self.remove_unattached_button = Button(label="Remove unattached nodes",
button_type="success")
self.remove_unattached_button.on_click(self.remove_unbound_nodes)
self.update_props_button = Button(label="Update Properties",
button_type="warning")
self.update_props_button.on_click(self.update_properties)
self.update_layout_button = Button(label="Update Layout",
button_type="success")
self.update_layout_button.on_click(self.update_layout)
self.select_centrality = Select(title="Centrality Metric:",
value="Degree Centrality",
options=list(
self.centrality_metrics.keys()))
self.select_centrality.on_change('value', self.update_centrality)
self.slider = Slider(start=0,
end=10,
value=0,
step=1,
title="Threshold %")
self.slider.on_change('value', self.filter_threshold)
self.slider.value = thresh_val
#self.filter_threshold('',0,3)
def reinit(self, network_file, layout_file, count_path, title):
lines_source = self.network_lines_tab1
nodes_source = self.nodes_sources_tab1
self.network_file = network_file
self.layout_file = layout_file
self.count_path = count_path
self.network_plots_n_circle_tab1[0].title.text = title
self.network_tuple = self.load_network(network_file, layout_file)
network, layout = self.network_tuple
print('loaded new network')
nodes, nodes_coordinates = zip(*sorted(layout.items()))
count_dict = dict(pickle.load(open(self.count_path, 'rb')))
nodes_xs, nodes_ys = list(zip(*nodes_coordinates))
node_occurances = [count_dict[node] for node in nodes]
nodes_source.data['x'] = nodes_xs
nodes_source.data['y'] = nodes_ys
nodes_source.data['name'] = nodes
nodes_source.data['counts'] = node_occurances
lines_source.data = self.get_edges_specs(network, layout)
self.update_properties()
self.slider.value = 8
self.filter_threshold('', 0, 8)
def load_network(self, network_file, layout_file):
network = pickle.load(open(network_file, 'rb'))
layout = pickle.load(open(layout_file, 'rb'))
return (network, layout)
def column_source(self, layout, count_path):
nodes, nodes_coordinates = zip(*sorted(layout.items()))
count_dict = dict(pickle.load(open(count_path, 'rb')))
nodes_xs, nodes_ys = list(zip(*nodes_coordinates))
node_occurances = [count_dict[node] for node in nodes]
nodes_source = ColumnDataSource(
dict(x=nodes_xs, y=nodes_ys, name=nodes, counts=node_occurances))
return nodes_source
def create_network_plot(self, nodes_source, title='', width=800):
plot = figure(plot_width=width,
plot_height=700,
tools=['tap', 'box_zoom', 'reset', 'pan', 'wheel_zoom'],
title=title)
plot.title.text_font = "helvica"
plot.title.text_font_style = "bold"
plot.title.text_font_size = "20px"
plot.background_fill_color = "beige"
plot.background_fill_alpha = 0.2
g1 = Circle(x='x', y='y', size=2, fill_color='blue')
g1_r = plot.add_glyph(source_or_glyph=nodes_source, glyph=g1)
g1_hover = HoverTool(renderers=[g1_r],
tooltips=[('name', '@name'),
('count', '@counts')])
glyph_text = Text(x="x",
y="y",
text="name",
text_color="#ff4a4a",
text_font_size='6pt',
text_alpha=0.7)
plot.add_glyph(nodes_source, glyph_text)
plot.add_tools(g1_hover)
plot.grid.grid_line_color = None
plot.axis.visible = False
return plot, g1_r, glyph_text
def get_edges_specs(self, _network, _layout):
d = dict(xs=[], ys=[], alphas=[])
weights = [d['weight'] for u, v, d in _network.edges(data=True)]
max_weight = max(weights)
calc_alpha = lambda h: 0.1 + 0.6 * (h / max_weight)
for u, v, data in _network.edges(data=True):
d['xs'].append([_layout[u][0], _layout[v][0]])
d['ys'].append([_layout[u][1], _layout[v][1]])
d['alphas'].append(calc_alpha(data['weight']))
return d
def add_lines(self, network_tuple, plot):
lines_source = ColumnDataSource(self.get_edges_specs(*network_tuple))
r_lines = plot.multi_line('xs',
'ys',
line_width=2,
alpha='alphas',
color='navy',
source=lines_source)
return lines_source
def get_centrality_n_community(self, network, nodes_source, g1_r):
community_colors = self.community_colors
centrality = networkx.algorithms.centrality.degree_centrality(network)
# first element, are nodes again
_, nodes_centrality = zip(*sorted(centrality.items()))
nodes_source.add(
[7 + 10 * t / max(nodes_centrality) for t in nodes_centrality],
'centrality')
partition = community.best_partition(network)
p_, nodes_community = zip(*sorted(partition.items()))
nodes_source.add(nodes_community, 'community')
nodes_source.add([community_colors[t % len(community_colors)]\
for t in nodes_community], 'community_color')
g1_r.glyph.size = 'centrality'
g1_r.glyph.fill_color = 'community_color'
def remove_node_1_net(self, nodes_source, lines_source, network, layout):
print('line 92')
print(type(nodes_source.selected['1d']['indices']))
print(len(nodes_source.selected['1d']['indices']))
if (nodes_source.selected['1d']['indices']):
idx = nodes_source.selected['1d']['indices'][0]
else:
return
# update networkX network object
node = nodes_source.data['name'][idx]
network.remove_node(node)
print('line 97')
# update layout
layout.pop(node)
# update nodes ColumnDataSource
new_source_data = dict()
for col in nodes_source.column_names:
print('line 104')
new_source_data[col] = [
e for i, e in enumerate(nodes_source.data[col]) if i != idx
]
nodes_source.data = new_source_data
# update lines ColumnDataSource
lines_source.data = self.get_edges_specs(network, layout)
def remove_node_tab1(self):
self.remove_node_1_net(self.nodes_sources_tab1,
self.network_lines_tab1, *self.network_tuple)
def remove_unbound_nodes(self):
network, layout = self.network_tuple
lines_source = self.network_lines_tab1
nodes_source = self.nodes_sources_tab1
unbound_nodes = []
for node in network.nodes():
if not network.edges(node):
unbound_nodes.append(node)
for node in unbound_nodes:
network.remove_node(node)
layout.pop(node)
nodes, nodes_coordinates = zip(*sorted(layout.items()))
count_dict = dict(pickle.load(open(self.count_path, 'rb')))
nodes_xs, nodes_ys = list(zip(*nodes_coordinates))
node_occurances = [count_dict[node] for node in nodes]
nodes_source.data['x'] = nodes_xs
nodes_source.data['y'] = nodes_ys
nodes_source.data['name'] = nodes
nodes_source.data['counts'] = node_occurances
self.update_properties()
lines_source.data = self.get_edges_specs(network, layout)
def update_properties(self):
community_colors = self.community_colors
network, layout = self.network_tuple
nodes_source = self.nodes_sources_tab1
partition = community.best_partition(network)
p_, nodes_community = zip(*sorted(partition.items()))
nodes_source.data['community'] = nodes_community
nodes_source.data['community_color'] = [
community_colors[t % len(community_colors)]
for t in nodes_community
]
centrality = self.centrality_metrics[self.select_centrality.value](
network, weight='weight')
_, nodes_centrality = zip(*sorted(centrality.items()))
nodes_source.data['centrality'] = [
7 + 10 * t / max(nodes_centrality) for t in nodes_centrality
]
def update_centrality(self, attrname, old, new):
network, _ = self.network_tuple
nodes_source = self.nodes_sources_tab1
centrality = self.centrality_metrics[self.select_centrality.value](
network, weight='weight')
_, nodes_centrality = zip(*sorted(centrality.items()))
nodes_source.data['centrality'] = [
7 + 10 * t / max(nodes_centrality) for t in nodes_centrality
]
def update_layout(self):
network, layout = self.network_tuple
lines_source = self.network_lines_tab1
nodes_source = self.nodes_sources_tab1
new_layout = networkx.spring_layout(network,
k=1.1 /
sqrt(network.number_of_nodes()),
iterations=100)
layout = new_layout
nodes, nodes_coordinates = zip(*sorted(layout.items()))
nodes_xs, nodes_ys = list(zip(*nodes_coordinates))
nodes_source.data['x'] = nodes_xs
nodes_source.data['y'] = nodes_ys
lines_source.data = self.get_edges_specs(network, layout)
def filter_threshold(self, attrname, old, new):
network, layout = self.network_tuple
if (old == new):
return
if (old > new):
self.network_tuple = self.load_network(self.network_file,
self.layout_file)
network, layout = self.network_tuple
weights = [d['weight'] for u, v, d in network.edges(data=True)]
max_weight = max(weights)
min_weight = min(weights)
threshold = (new * (max_weight - min_weight) / 100.0)
to_remove_list = []
sources_in = set()
for (u, v, d) in network.edges(data='weight'):
if (d < threshold):
if (((u, v, d) in sources_in) or ((v, u, d) in sources_in)):
continue
to_remove_list.append((u, v))
sources_in.add((u, v, d))
network.remove_edges_from(to_remove_list)
self.remove_unbound_nodes()
font_size = 6 + new
font_size = min(10, font_size)
self.network_plots_n_circle_tab1[2].text_font_size = '{}pt'.format(
font_size)
self.update_layout()
def return_view(self):
return column(self.network_plots_n_circle_tab1[0],row(widgetbox(self.slider,self.select_centrality),\
widgetbox(self.drop_button_tab1,self.remove_unattached_button),\
widgetbox(self.update_props_button, self.update_layout_button,)))
"""
GraVE Documentation
-------------------
"""
import networkx as nx
from networkx.algorithms.centrality import closeness_centrality
import matplotlib.pyplot as plt
from grave import plot_network, use_attributes
toy_network = nx.barbell_graph(10, 14)
toy_centrality = closeness_centrality(toy_network)
max_centrality = max(toy_centrality.values())
for u, v, edge_attributes in toy_network.edges.data():
c = (toy_centrality[u] + toy_centrality[v]) / 2
color_idx = (c / max_centrality)
cmap = plt.get_cmap()
edge_attributes['color'] = cmap(color_idx)
edge_attributes['width'] = 2
for node, node_attributes in toy_network.nodes.data():
node_attributes['size'] = (1 - (toy_centrality[node] / max_centrality) +
.1) * 100
def edge_style(edge_attributes):
return {'linewidth': edge_attributes.get('weight', 1)}
def calculate_all_centralities(data):
"""
Calculates all four centralities metrics for the input graph
Paramaters:
data: a json object which represents the graph.
This json is manipulated and the necessary metrics are added to it.
"""
G = json_graph.node_link_graph(
data) #loads the data to a NetworkX graph object
#Calculates three of the metrics
degree = centrality.degree_centrality(G)
closeness = centrality.closeness_centrality(G)
betweeness = centrality.betweenness_centrality(G)
eigenvector_fail = False
try: #Eigenvector centrality can fail to converge.
eigenvector = centrality.eigenvector_centrality(DiGraph(G),
max_iter=100000)
except NetworkXError: #Eigenvector values will be None if calculation fails.
eigenvector = []
eigenvector_fail = True
print "Max iterations exceeded"
degree_max = -1.0
closeness_max = -1.0
betweeness_max = -1.0
eigenvector_max = -1.0
for author in data['nodes']: #Adds the unnormalized values in the json
i = author['id']
author['degreeCentralityUnnormalized'] = degree[i]
author['closenessCentralityUnnormalized'] = closeness[i]
author['betweennessCentralityUnnormalized'] = betweeness[i]
author['eigenvectorCentralityUnnormalized'] = eigenvector[
i] if not eigenvector_fail else 1.0
#Finds the highest values for each centrality type
for i in degree:
if degree[i] > degree_max:
degree_max = degree[i]
for i in closeness:
if closeness[i] > closeness_max:
closeness_max = closeness[i]
for i in betweeness:
if betweeness[i] > betweeness_max:
betweeness_max = betweeness[i]
for i in eigenvector:
if eigenvector[i] > eigenvector_max:
eigenvector_max = eigenvector[i]
#Normalizes the values
for i in degree:
if degree[i] != 0:
degree[i] = degree[i] / degree_max
for i in closeness:
if closeness[i] != 0:
closeness[i] = closeness[i] / closeness_max
for i in betweeness:
if betweeness[i] != 0:
betweeness[i] = betweeness[i] / betweeness_max
for i in eigenvector:
if eigenvector[i] != 0:
eigenvector[i] = eigenvector[i] / eigenvector_max
#Adds the normalized values to the json
for author in data['nodes']:
i = author['id']
author['degreeCentrality'] = degree[i]
author['closenessCentrality'] = closeness[i]
author['betweennessCentrality'] = betweeness[i]
author['eigenvectorCentrality'] = eigenvector[
i] if not eigenvector_fail else 1.0
return data
def analyze(directed_df, undirected_df, auxiliary_df):
directed_df = directed_df.copy(deep=True)
undirected_df = undirected_df.copy(deep=True)
directed_df = directed_df.rename(mapper=lambda name: name.lower(),
axis='columns')
undirected_df = undirected_df.rename(mapper=lambda name: name.lower(),
axis='columns')
G = nx.from_pandas_edgelist(directed_df,
edge_attr=['weight', 'change'],
create_using=nx.DiGraph)
G_undirected = nx.from_pandas_edgelist(undirected_df,
edge_attr=['weight', 'change'])
alpha_coef = 0.9
alpha = alpha_coef / max(nx.adjacency_spectrum(G).real)
alpha_undirected = alpha_coef / max(
nx.adjacency_spectrum(G_undirected).real)
centralities = {
'out_degree':
weighted_degree_centrality(G),
'in_degree':
weighted_degree_centrality(G.reverse()),
'undirected_degree':
weighted_degree_centrality(G_undirected),
'out_eigenvector':
centrality.eigenvector_centrality(G, weight='weight'),
'in_eigenvector':
centrality.eigenvector_centrality(G.reverse(), weight='weight'),
'undirected_eigenvector':
centrality.eigenvector_centrality(G_undirected, weight='weight'),
'out_closeness':
centrality.closeness_centrality(G, distance='weight'),
'in_closeness':
centrality.closeness_centrality(G.reverse(), distance='weight'),
'undirected_closeness':
centrality.closeness_centrality(G_undirected, distance='weight'),
'out_betweenness':
centrality.betweenness_centrality(G, weight='weight'),
'in_betweenness':
centrality.betweenness_centrality(G.reverse(), weight='weight'),
'undirected_betweenness':
centrality.betweenness_centrality(G_undirected, weight='weight'),
'out_katz':
centrality.katz_centrality(G, alpha=alpha, weight='weight'),
'in_katz':
centrality.katz_centrality(G.reverse(), alpha=alpha, weight='weight'),
'undirected_katz':
centrality.katz_centrality(G_undirected, alpha=alpha, weight='weight')
}
for centrality_type in centralities.keys():
directed_df[centrality_type] = np.NaN
augmented_auxiliary_df = auxiliary_df.copy(deep=True)
for key, row in augmented_auxiliary_df.iterrows():
node = row['docid']
for centrality_type, values in centralities.items():
if node in values:
augmented_auxiliary_df.at[key, centrality_type] = values[node]
print(augmented_auxiliary_df)
return augmented_auxiliary_df
def run_GT_calcs(G, just_data, Do_kdist, Do_dia, Do_BCdist, Do_CCdist, Do_ECdist, Do_GD, Do_Eff, \
Do_clust, Do_ANC, Do_Ast, Do_WI, multigraph):
# getting nodes and edges and defining variables for later use
klist = [0]
Tlist = [0]
BCdist = [0]
CCdist = [0]
ECdist = [0]
if multigraph:
Do_BCdist = 0
Do_ECdist = 0
Do_clust = 0
data_dict = {"x": [], "y": []}
nnum = int(nx.number_of_nodes(G))
enum = int(nx.number_of_edges(G))
if Do_ANC | Do_dia:
connected_graph = nx.is_connected(G)
# making a dictionary for the parameters and results
just_data.append(nnum)
data_dict["x"].append("Number of nodes")
data_dict["y"].append(nnum)
just_data.append(enum)
data_dict["x"].append("Number of edges")
data_dict["y"].append(enum)
multi_image_settings.progress(35)
# calculating parameters as requested
# creating degree histogram
if (Do_kdist == 1):
klist1 = nx.degree(G)
ksum = 0
klist = np.zeros(len(klist1))
for j in range(len(klist1)):
ksum = ksum + klist1[j]
klist[j] = klist1[j]
k = ksum / len(klist1)
k = round(k, 5)
just_data.append(k)
data_dict["x"].append("Average degree")
data_dict["y"].append(k)
multi_image_settings.progress(40)
# calculating network diameter
if (Do_dia == 1):
if connected_graph:
dia = int(diameter(G))
else:
dia = 'NaN'
just_data.append(dia)
data_dict["x"].append("Network Diameter")
data_dict["y"].append(dia)
multi_image_settings.progress(45)
# calculating graph density
if (Do_GD == 1):
GD = nx.density(G)
GD = round(GD, 5)
just_data.append(GD)
data_dict["x"].append("Graph density")
data_dict["y"].append(GD)
multi_image_settings.progress(50)
# calculating global efficiency
if (Do_Eff == 1):
Eff = global_efficiency(G)
Eff = round(Eff, 5)
just_data.append(Eff)
data_dict["x"].append("Global Efficiency")
data_dict["y"].append(Eff)
multi_image_settings.progress(55)
if (Do_WI == 1):
WI = wiener_index(G)
WI = round(WI, 1)
just_data.append(WI)
data_dict["x"].append("Wiener Index")
data_dict["y"].append(WI)
multi_image_settings.progress(60)
# calculating clustering coefficients
if (Do_clust == 1):
Tlist1 = clustering(G)
Tlist = np.zeros(len(Tlist1))
for j in range(len(Tlist1)):
Tlist[j] = Tlist1[j]
clust = average_clustering(G)
clust = round(clust, 5)
just_data.append(clust)
data_dict["x"].append("Average clustering coefficient")
data_dict["y"].append(clust)
# calculating average nodal connectivity
if (Do_ANC == 1):
if connected_graph:
ANC = average_node_connectivity(G)
ANC = round(ANC, 5)
else:
ANC = 'NaN'
just_data.append(ANC)
data_dict["x"].append("Average nodal connectivity")
data_dict["y"].append(ANC)
multi_image_settings.progress(65)
# calculating assortativity coefficient
if (Do_Ast == 1):
Ast = degree_assortativity_coefficient(G)
Ast = round(Ast, 5)
just_data.append(Ast)
data_dict["x"].append("Assortativity Coefficient")
data_dict["y"].append(Ast)
multi_image_settings.progress(70)
# calculating betweenness centrality histogram
if (Do_BCdist == 1):
BCdist1 = betweenness_centrality(G)
Bsum = 0
BCdist = np.zeros(len(BCdist1))
for j in range(len(BCdist1)):
Bsum += BCdist1[j]
BCdist[j] = BCdist1[j]
Bcent = Bsum / len(BCdist1)
Bcent = round(Bcent, 5)
just_data.append(Bcent)
data_dict["x"].append("Average betweenness centrality")
data_dict["y"].append(Bcent)
multi_image_settings.progress(75)
# calculating closeness centrality
if (Do_CCdist == 1):
CCdist1 = closeness_centrality(G)
Csum = 0
CCdist = np.zeros(len(CCdist1))
for j in range(len(CCdist1)):
Csum += CCdist1[j]
CCdist[j] = CCdist1[j]
Ccent = Csum / len(CCdist1)
Ccent = round(Ccent, 5)
just_data.append(Ccent)
data_dict["x"].append("Average closeness centrality")
data_dict["y"].append(Ccent)
multi_image_settings.progress(80)
# calculating eigenvector centrality
if (Do_ECdist == 1):
try:
ECdist1 = eigenvector_centrality(G, max_iter=100)
except:
ECdist1 = eigenvector_centrality(G, max_iter=10000)
Esum = 0
ECdist = np.zeros(len(ECdist1))
for j in range(len(ECdist1)):
Esum += ECdist1[j]
ECdist[j] = ECdist1[j]
Ecent = Esum / len(ECdist1)
Ecent = round(Ccent, 5)
just_data.append(Ecent)
data_dict["x"].append("Average eigenvector centrality")
data_dict["y"].append(Ecent)
data = pd.DataFrame(data_dict)
return data, just_data, klist, Tlist, BCdist, CCdist, ECdist
def compute_features(self):
# Degree centrality
degree_centrality = lambda graph: list(
centrality.degree_centrality(graph).values())
self.add_feature(
"degree centrality",
degree_centrality,
"The degree centrality distribution",
InterpretabilityScore(5),
statistics="centrality",
)
# Betweenness Centrality
betweenness_centrality = lambda graph: list(
centrality.betweenness_centrality(graph).values())
self.add_feature(
"betweenness centrality",
betweenness_centrality,
"Betweenness centrality of a node v is the sum of the fraction of \
all-pairs shortest paths that pass through v",
InterpretabilityScore(5),
statistics="centrality",
)
# Closeness centrality
closeness_centrality = lambda graph: list(
centrality.closeness_centrality(graph).values())
self.add_feature(
"closeness centrality",
closeness_centrality,
"Closeness is the reciprocal of the average shortest path distance",
InterpretabilityScore(5),
statistics="centrality",
)
# Edge betweenness centrality
def edge_betweenness_centrality(graph):
if graph.edges:
return list(
centrality.edge_betweenness_centrality(graph).values())
return [np.nan]
self.add_feature(
"edge betweenness centrality",
edge_betweenness_centrality,
"Betweenness centrality of an edge e is the sum of the fraction of \
all-pairs shortest paths that pass through e",
InterpretabilityScore(4),
statistics="centrality",
)
# Harmonic centrality
harmonic_centrality = lambda graph: list(
centrality.harmonic_centrality(graph).values())
self.add_feature(
"harmonic centrality",
harmonic_centrality,
"Harmonic centrality of a node u is the sum of the reciprocal \
of the shortest path distances from all other nodes to u",
InterpretabilityScore(4),
statistics="centrality",
)
# Subgraph centrality
subgraph_centrality = lambda graph: list(
centrality.subgraph_centrality(graph).values())
self.add_feature(
"subgraph centrality",
subgraph_centrality,
"The subgraph centrality for a node is the sum of weighted closed walks \
of all lengths starting and ending at that node.",
InterpretabilityScore(3),
statistics="centrality",
)
# Second order centrality
second_order_centrality = lambda graph: list(
centrality.second_order_centrality(utils.ensure_connected(graph)).
values())
self.add_feature(
"second order centrality",
second_order_centrality,
"The second order centrality of a given node is the standard deviation \
of the return times to that node of a perpetual random walk on G",
InterpretabilityScore(4),
statistics="centrality",
)
# Eigenvector centrality
eigenvector_centrality = lambda graph: list(
centrality.eigenvector_centrality_numpy(
utils.ensure_connected(graph)).values())
self.add_feature(
"eigenvector centrality",
eigenvector_centrality,
"Eigenvector centrality computes the centrality for a node based \
on the centrality of its neighbors",
InterpretabilityScore(4),
statistics="centrality",
)
# Katz centrality
katz_centrality = lambda graph: list(
centrality.katz_centrality_numpy(utils.ensure_connected(graph)).
values())
self.add_feature(
"katz centrality",
katz_centrality,
"Generalisation of eigenvector centrality - Katz centrality computes the \
centrality for a node based on the centrality of its neighbors",
InterpretabilityScore(4),
statistics="centrality",
)
# Page Rank
pagerank = lambda graph: list(nx.pagerank_numpy(graph).values())
self.add_feature(
"pagerank",
pagerank,
"The pagerank computes a ranking of the nodes in the graph based on \
the structure of the incoming links. ",
InterpretabilityScore(4),
statistics="centrality",
)
corpo_pairs_list = open('./data/corpo_pairs_res.txt').readlines()
G = nx.Graph()
name_index_list = {}
index = 0
for _pair in corpo_pairs_list:
_, pair_a, _, pair_b, _ = _pair.split(',')
if pair_a not in name_index_list:
name_index_list[pair_a] = str(index)
index += 1
if pair_b not in name_index_list:
name_index_list[pair_b] = str(index)
index += 1
# print(pair_a + ',' + pair_b)
G.add_edge(pair_a, pair_b)
# nx.draw(G)
# plt.savefig("path.png")
from networkx.algorithms.centrality import degree_centrality, closeness_centrality,betweenness_centrality,communicability_betweenness_centrality
degree_res = degree_centrality(G)
closeness_res = closeness_centrality(G)
betweenness_res = communicability_betweenness_centrality(G)
centrality_out = open('./data/centrality.txt', 'w')
centrality_out.write('pattern,degree,closeness,betweenness')
for key, value in degree_res.items():
centrality_out.write('\n' + key + '\t %.2f \t %.2f \t %.2f'%(value,closeness_res[key],betweenness_res[key]))
centrality_out.close()
print(degree_res)
print(closeness_res)
print(betweenness_res)
p_, nodes_community = zip(*sorted(partition.items()))
nodes_source.data['community'] = nodes_community
nodes_source.data['community_color'] = [community_colors[t % len(community_colors)]
for t in nodes_community]
centrality = centrality_metrics[select_centrality.value](network, weight='weight')
_, nodes_centrality = zip(*sorted(centrality.items()))
nodes_source.data['centrality'] = [7 + 10 * t / max(nodes_centrality) for t in nodes_centrality]
update_props_button = Button(label="Update Properties")
update_props_button.on_click(update_properties)
centrality_metrics = {"Degree Centrality":
lambda n, weight=_: centrality_algorithms.degree_centrality(n),
"Closeness Centrality":
lambda n, weight=_: centrality_algorithms.closeness_centrality(n),
"Betweenness Centrality":
centrality_algorithms.betweenness_centrality}
def update_centrality(attrname, old, new):
centrality = centrality_metrics[select_centrality.value](network, weight='weight')
_, nodes_centrality = zip(*sorted(centrality.items()))
nodes_source.data['centrality'] = [7 + 10 * t / max(nodes_centrality) for t in nodes_centrality]
select_centrality = Select(title="Centrality Metric:", value="Degree Centrality",
options=list(centrality_metrics.keys()))
select_centrality.on_change('value', update_centrality)
def closenesscentrality(self, brain, outfilebase="brain", append=True):
""" Calculates node and hub closeness centralities. For hub centralities there are two files, one with the values in and another
with the hub identities in corresponding rows.
"""
## closeness centrality
# node centrality
outfile = outfilebase + '_closeness_centralities_nodes'
boolVal = self.fileCheck(outfile, append)
# open file and write headers if necessary
if append and boolVal:
f = open(outfile, "ab")
headers = None
else:
f = open(outfile, "wb")
headers = dict((n, n) for n in brain.G.nodes())
writeObj = DictWriter(f, fieldnames=brain.G.nodes())
if headers:
writeObj.writerow(headers)
# make calculations
centralities = centrality.closeness_centrality(
brain.G) # calculate centralities for largest connected component
nodecentralitiestowrite = dict(
(n, None) for n in brain.G.nodes()
) # create a blank dictionary of all nodes in the graph
for node in centralities:
nodecentralitiestowrite[node] = centralities[
node] # populate the blank dictionary with centrality values
writeObj.writerow(
nodecentralitiestowrite) # write out centrality values
f.close()
# hub centrality
outfile = outfilebase + '_closeness_centralities_hubs'
hubidfile = outfilebase + '_closeness_centralities_hubs_ids'
OFbool = self.fileCheck(outfile, append)
self.fileCheck(hubidfile, append)
if append and OFbool:
f = open(outfile, "ab")
g = open(hubidfile, "ab")
else:
f = open(outfile, "wb")
g = open(hubidfile, "wb")
centhubs = [hub for hub in brain.hubs if hub in brain.G
] # hubs within largest connected graph component
# write hub identifies to file
writeObj = DictWriter(f, fieldnames=brain.hubs)
hubwriter = DictWriter(g, fieldnames=brain.hubs)
headers = dict(
(n, n)
for n in brain.hubs) # dictionary of all hubs in network to write
hubwriter.writerow(headers)
hubcentralitieistowrite = dict(
(n, None) for n in
brain.hubs) # empty dictionary to populate with centralities data
for hub in centhubs:
hubcentralitieistowrite[hub] = nodecentralitiestowrite[hub]
writeObj.writerow(hubcentralitieistowrite)
f.close()
g.close()
def closeness_centrality(graph):
"""closeness_centrality"""
return list(centrality.closeness_centrality(graph).values())
def calculate_all_centralities(data):
"""
Calculates all four centralities metrics for the input graph
Paramaters:
data: a json object which represents the graph.
This json is manipulated and the necessary metrics are added to it.
"""
G = json_graph.node_link_graph(data) #loads the data to a NetworkX graph object
#Calculates three of the metrics
degree = centrality.degree_centrality(G)
closeness = centrality.closeness_centrality(G)
betweeness = centrality.betweenness_centrality(G)
eigenvector_fail = False
try: #Eigenvector centrality can fail to converge.
eigenvector = centrality.eigenvector_centrality(DiGraph(G),max_iter=100000)
except NetworkXError: #Eigenvector values will be None if calculation fails.
eigenvector = []
eigenvector_fail = True
print "Max iterations exceeded"
degree_max = -1.0
closeness_max = -1.0
betweeness_max = -1.0
eigenvector_max = -1.0
for author in data['nodes']: #Adds the unnormalized values in the json
i = author['id']
author['degreeCentralityUnnormalized'] = degree[i]
author['closenessCentralityUnnormalized'] = closeness[i]
author['betweennessCentralityUnnormalized'] = betweeness[i]
author['eigenvectorCentralityUnnormalized'] = eigenvector[i] if not eigenvector_fail else 1.0
#Finds the highest values for each centrality type
for i in degree:
if degree[i]>degree_max:
degree_max = degree[i]
for i in closeness:
if closeness[i]>closeness_max:
closeness_max = closeness[i]
for i in betweeness:
if betweeness[i]>betweeness_max:
betweeness_max = betweeness[i]
for i in eigenvector:
if eigenvector[i]>eigenvector_max:
eigenvector_max = eigenvector[i]
#Normalizes the values
for i in degree:
if degree[i] != 0:
degree[i] = degree[i]/degree_max
for i in closeness:
if closeness[i] != 0:
closeness[i] = closeness[i]/closeness_max
for i in betweeness:
if betweeness[i] != 0:
betweeness[i] = betweeness[i]/betweeness_max
for i in eigenvector:
if eigenvector[i] != 0:
eigenvector[i] = eigenvector[i]/eigenvector_max
#Adds the normalized values to the json
for author in data['nodes']:
i = author['id']
author['degreeCentrality'] = degree[i]
author['closenessCentrality'] = closeness[i]
author['betweennessCentrality'] = betweeness[i]
author['eigenvectorCentrality'] = eigenvector[i] if not eigenvector_fail else 1.0
return data
def get_metric_from_graph(self, metric=None, nedges=None, keyword=None, graph=None, month=None):
#'''this func will do most of the work. lets you get a named metric for nodes, optionally restricting this by month, by specified nodes, or by entity type ie lobby/staffer/lobbyist/commissioner. first constructs a cache key and then looks in the cache
ck = str(metric) + str(month) + str(keyword)
if ck in self.cache:
return self.cache[ck]
g = graph
if keyword:
nedges = [node for node in g.nodes_iter(data=True) if node[1]['type'] == keyword]
#'''if a keyword search is specified, we list the nodes where that keyword is found in one of its attributes'''
if metric == u'Degree':
upshot = self.degree(g, nedges)
if metric == u'Gatekeepership':
upshot = self.gatekeeper(g, nedges)
if metric == u'Closeness Centrality':
u = centrality.closeness_centrality(g, normalized=True)
if nedges:
filter_list = [n[0] for n in nedges]
upshot = {g.node[k]['name']: v for k,v in u.items() if k in filter_list}
else:
upshot = {g.node[k]['name']: v for k,v in u.items()}
if metric == u'Betweenness':
u = centrality.betweenness_centrality(g, weight='weight', normalized=True)
if nedges:
filter_list = [n[0] for n in nedges]
upshot = {g.node[k]['name']: v for k,v in u.items() if k in filter_list}
else:
upshot = {g.node[k]['name']: v for k,v in u.items()}
if metric == u'Greedy_Fragile':
upshot = self.greedy_fragile(g, nedges)
if metric == u'Link Centrality':
u = centrality.edge_betweenness_centrality(g, weight='weight', normalized=True)
upshot = {}
for k, v in u.items(): # doing it in a similar way to the other linkwise metric below.
a, b = k
c = g.node[a]['name']
d = g.node[b]['name']
if nedges:
filter_list = [n[0] for n in nedges]
if a in filter_list or b in filter_list:
upshot[unicode(c + ' - ' + d)] = v
else:
upshot[unicode(a + ' - ' + b)] = v
if metric == u'Predicted Links':
gr = self.make_unigraph_from_multigraph(mg=g)
u = link_prediction.resource_allocation_index(gr)
upshot = {}
for k, v, p in u:
if p > 0: #RAI examines all nonexistent edges in graph and will return all of them, including ones with a zero index. we therefore filter for positive index values.
a = g.node[k]['name']
b = g.node[v]['name']
if nedges:
filter_list = [n[0] for n in nedges]
if k in filter_list or v in filter_list:
upshot[unicode(a + ' - ' + b)] = p
else:
upshot[unicode(a + ' - ' + b)] = p
self.cacheflow(ck, data=upshot)
return upshot
