def effective_diameter_over_time():
effective_diameters = {}
slices = graphgen.generate_weighted_time_slices()
graph = nx.MultiDiGraph()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
effective_diam = graphtools.effective_diameter(graph)
effective_diameters[end_day] = effective_diam
print end_day, effective_diam
utils.save_node_map(effective_diameters, ("effective_diameters"))
def effective_diameter_over_time():
effective_diameters = {}
slices = graphgen.generate_weighted_time_slices()
graph = nx.MultiDiGraph()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
effective_diam = graphtools.effective_diameter(graph)
effective_diameters[end_day] = effective_diam
print end_day, effective_diam
utils.save_node_map(effective_diameters, ("effective_diameters"))
def frac_nodes_in_gcc_over_time():
frac_nodes_in_gcc = {}
graph = nx.MultiDiGraph()
slices = graphgen.generate_weighted_time_slices()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(graph)
frac_nodes_in_gcc[end_day] = frac_nodes
print end_day, frac_nodes
utils.save_node_map(frac_nodes_in_gcc, ("frac_nodes_in_gcc"))
def frac_nodes_in_gcc_over_time():
frac_nodes_in_gcc = {}
graph = nx.MultiDiGraph()
slices = graphgen.generate_weighted_time_slices()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(graph)
frac_nodes_in_gcc[end_day] = frac_nodes
print end_day, frac_nodes
utils.save_node_map(frac_nodes_in_gcc, ("frac_nodes_in_gcc"))
def nodes_vs_edges_over_time():
num_nodes = []
num_edges = []
slices = graphgen.generate_weighted_time_slices()
graph = nx.MultiDiGraph()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
nodes, edges = graphtools.get_num_nodes_edges_from_graph(graph)
num_nodes.append(nodes)
num_edges.append(edges)
print nodes, edges
utils.save_lists(num_nodes, num_edges, ("nodes_vs_edges"))
def nodes_vs_edges_over_time():
num_nodes = []
num_edges = []
slices = graphgen.generate_weighted_time_slices()
graph = nx.MultiDiGraph()
for end_day in graphgen._days:
start = int(str(end_day) + "000000")
end = int(str(end_day) + "235959")
graph = graphgen.add_slice_to_graph(graph, start, end)
nodes, edges = graphtools.get_num_nodes_edges_from_graph(graph)
num_nodes.append(nodes)
num_edges.append(edges)
print nodes, edges
utils.save_lists(num_nodes, num_edges, ("nodes_vs_edges"))
def avg_out_degree_over_time():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees_r = {}
avg_out_dgree_p = {}
for start, end in slices:
n, m = _node_and_edges(start, end)
if n == 0:
continue
rg = nx.gnm_random_graph(n, m, directed=True)
pfg = nx.barabasi_albert_graph(n, m/n)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(rg)
avg_out_degrees_r[start] = avg_out_deg
avg_out_deg = graphtools.get_avg_out_degree_from_graph(pfg)
avg_out_degrees_p[start] = avg_out_deg
utils.save_node_map(avg_out_degrees_r, ("avg_out_degrees_r"))
utils.save_node_map(avg_out_degrees_p, ("avg_out_degrees_p"))
def avg_out_degree_over_time():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees_r = {}
avg_out_dgree_p = {}
for start, end in slices:
n, m = _node_and_edges(start, end)
if n == 0:
continue
rg = nx.gnm_random_graph(n, m, directed=True)
pfg = nx.barabasi_albert_graph(n, m / n)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(rg)
avg_out_degrees_r[start] = avg_out_deg
avg_out_deg = graphtools.get_avg_out_degree_from_graph(pfg)
avg_out_degrees_p[start] = avg_out_deg
utils.save_node_map(avg_out_degrees_r, ("avg_out_degrees_r"))
utils.save_node_map(avg_out_degrees_p, ("avg_out_degrees_p"))
def all_computations():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees_r = {}
# effective_diameters_p = {}
# effective_diameters_r = {}
frac_nodes_in_gcc_p = {}
frac_nodes_in_gcc_r = {}
i = 0
for start, end in slices:
print 'slice %s of %s' % (i, len(slices))
n, m = _node_and_edges(start, end)
if n == 0:
continue
rg = nx.gnm_random_graph(n, m, directed=True)
pfg = nx.barabasi_albert_graph(n, m/n)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(rg)
avg_out_degrees_r[start] = avg_out_deg
# effective_diam = graphtools.effective_diameter(rg)
# effective_diameters_r[start] = effective_diam
# effective_diam = graphtools.effective_diameter(pfg)
# effective_diameters_p[start] = effective_diam
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(rg)
frac_nodes_in_gcc_r[start] = frac_nodes
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(pfg)
frac_nodes_in_gcc_p[start] = frac_nodes
utils.save_node_map(avg_out_degrees_r, ("avg_out_degrees_r"))
# utils.save_node_map(effective_diameters_r, ("effective_diameters_r"))
# utils.save_node_map(effective_diameters_p, ("effective_diameters_p"))
utils.save_node_map(frac_nodes_in_gcc_r, ("frac_nodes_in_gcc_r"))
utils.save_node_map(frac_nodes_in_gcc_p, ("frac_nodes_in_gcc_p"))
def all_computations():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees = {}
frac_nodes_in_gcc = {}
num_nodes = []
num_edges = []
for i in range(len(slices)):
print i, " slices out of ", len(slices)
start, end = slices[i]
g = cv_from_btc(start, end)
if len(g) == 0:
continue
num_nodes.append(g.number_of_nodes())
num_edges.append(g.number_of_edges())
avg_out_deg = graphtools.get_avg_out_degree_from_graph(g)
avg_out_degrees[start] = avg_out_deg
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(g)
frac_nodes_in_gcc[start] = frac_nodes
utils.save_node_map(avg_out_degrees, ("avg_out_degrees_model"))
utils.save_node_map(frac_nodes_in_gcc, ("frac_nodes_in_gcc_model"))
utils.save_lists(num_nodes, num_edges, ("nodes_vs_edges_model"))
def all_computations():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees = {}
frac_nodes_in_gcc = {}
num_nodes = []
num_edges = []
for i in range(len(slices)):
print i, " slices out of ", len(slices)
start, end = slices[i]
g = cv_from_btc(start, end)
if len(g) == 0:
continue
num_nodes.append(g.number_of_nodes())
num_edges.append(g.number_of_edges())
avg_out_deg = graphtools.get_avg_out_degree_from_graph(g)
avg_out_degrees[start] = avg_out_deg
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(g)
frac_nodes_in_gcc[start] = frac_nodes
utils.save_node_map(avg_out_degrees, ("avg_out_degrees_model"))
utils.save_node_map(frac_nodes_in_gcc, ("frac_nodes_in_gcc_model"))
utils.save_lists(num_nodes, num_edges, ("nodes_vs_edges_model"))
def all_computations():
slices = graphgen.generate_weighted_time_slices()
avg_out_degrees_r = {}
# effective_diameters_p = {}
# effective_diameters_r = {}
frac_nodes_in_gcc_p = {}
frac_nodes_in_gcc_r = {}
i = 0
for start, end in slices:
print 'slice %s of %s' % (i, len(slices))
n, m = _node_and_edges(start, end)
if n == 0:
continue
rg = nx.gnm_random_graph(n, m, directed=True)
pfg = nx.barabasi_albert_graph(n, m / n)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(rg)
avg_out_degrees_r[start] = avg_out_deg
# effective_diam = graphtools.effective_diameter(rg)
# effective_diameters_r[start] = effective_diam
# effective_diam = graphtools.effective_diameter(pfg)
# effective_diameters_p[start] = effective_diam
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(rg)
frac_nodes_in_gcc_r[start] = frac_nodes
frac_nodes = graphtools.get_frac_nodes_in_gcc_from_graph(pfg)
frac_nodes_in_gcc_p[start] = frac_nodes
utils.save_node_map(avg_out_degrees_r, ("avg_out_degrees_r"))
# utils.save_node_map(effective_diameters_r, ("effective_diameters_r"))
# utils.save_node_map(effective_diameters_p, ("effective_diameters_p"))
utils.save_node_map(frac_nodes_in_gcc_r, ("frac_nodes_in_gcc_r"))
utils.save_node_map(frac_nodes_in_gcc_p, ("frac_nodes_in_gcc_p"))
print 'finished %s tag over time' % i
i += 1
# utils.save_lists(dates, deg_connectivity, stamp='gnp_deg_conn')
utils.save_lists(dates, avg_clust, stamp='gnp_avg_clust')
utils.save_lists(dates, lccs, stamp='gnp_lccs')
utils.save_lists(dates, largest_scc, stamp='gnp_largest_scc')
utils.save_lists(dates, avg_clust_p, stamp='pfg_avg_clust')
utils.save_lists(dates, lccs_p, stamp='pfg_lccs')
# plot single values over time
# Gini coefficient (way to get single number from distribution)
'''
_HMS = graphgen._HMS
slices = graphgen.generate_weighted_time_slices()
i = 0
dates = []
avg_clust = []
lccs = []
largest_scc = []
for start, end in slices:
g = cv_from_btc(start * _HMS, end * _HMS)
if len(g) == 0:
continue
stamp = str(start) + '_' + str(end)
tags_over_time.user_transaction_frequency(g, 'model_' + stamp)
tags_over_time.user_buy_frequency(g, 'model_' + stamp)
tags_over_time.user_sell_frequency(g, 'model_' + stamp)