def __init__(self):
self.dbu = db_manager.DatabaseUtility()
self.employee_data = self.dbu.get_eid()
self.employees = self.get_employees()
self.comm_matrix = [[0 for i in range(len(self.employee_data))]
for j in range(len(self.employee_data))]
self.g = nx.Graph()
def closeness(adj_table):
dbu = db_manager.DatabaseUtility()
employee_data = dbu.get_eid()
nnodes = len(adj_table)
metric_closeness = [0] * nnodes
for node in range(nnodes):
shortest_path_sum = 0
pred = bfs(adj_table, node)
for edge in range(nnodes):
shortest_path_sum += get_shortest_path(pred, edge)
label = employee_data[node][1] + ' ' + employee_data[node][2]
# Prevent division by zero error
if shortest_path_sum > 0:
metric_closeness[node] = (label, (count_nodes(adj_table) - 1) /
shortest_path_sum, node)
else:
metric_closeness[node] = (label, 0, node)
metric_closeness.sort(key=lambda x: x[1], reverse=True)
return metric_closeness
def betweenness(adj_table):
try:
metric_betweenness = load("betweenness.npy")
return metric_betweenness
except:
dbu = db_manager.DatabaseUtility()
employee_data = dbu.get_eid()
nnodes = len(adj_table)
graph = [set() for _ in range(nnodes)]
for i in range(nnodes):
for j in range(len(adj_table[i])):
graph[i].add(adj_table[i][j])
metric_betweennness = [0] * nnodes
for i in range(nnodes):
for j in range(nnodes):
if i != j:
paths = bfs_paths(graph, i, j)
counter = [0] * nnodes
for row in paths:
for k in range(nnodes):
if k != i and k != j and k in row:
counter[k] += 1
for node in range(nnodes):
# Prevent division by zero
if len(paths) > 0:
metric_betweennness[node] += counter[node] / len(
paths)
for node in range(len(metric_betweennness)):
label = employee_data[node][1] + ' ' + employee_data[node][2]
metric_betweennness[node] = (label, metric_betweennness[node] /
((count_nodes(adj_table) - 1) *
(count_nodes(adj_table) - 2)), node)
metric_betweennness.sort(key=lambda x: x[1], reverse=True)
save("betweenness", metric_betweennness)
return metric_betweennness
def eigenvector(p_matrix, a, epsilon):
dbu = db_manager.DatabaseUtility()
employee_data = dbu.get_eid()
q = p_matrix
p = AxP(a, p_matrix)
l = norm(p)
p = PxL(p, 1 / l)
while norm(PminusQ(p, q)) > epsilon:
q = p
p = AxP(a, p)
l = norm(p)
p = PxL(p, 1 / l)
for node in range(len(p)):
label = employee_data[node][1] + ' ' + employee_data[node][2]
p[node] = (label, p[node], node)
p.sort(key=lambda x: x[1], reverse=True)
return p
def __init__(self):
QtWidgets.QMainWindow.__init__(self)
self.dbu = db_manager.DatabaseUtility()
self.graph = network.SocialNetwork()
def __init__(self, database, table_name):
QDialog.__init__(self)
self.dbu = db_manager.DatabaseUtility(database, table_name)
self.setupUi(self)