def avg_out_degree_over_time():
avg_out_degrees = {}
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)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(graph)
avg_out_degrees[end_day] = avg_out_deg
print end_day, avg_out_deg
utils.save_node_map(avg_out_degrees, ("avg_out_degrees"))
def frac_nodes_in_gcc_over_time():
frac_nodes_in_gcc = {}
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)
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 effective_diameter_over_time():
effective_diameters = {}
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 avg_out_degree_over_time():
avg_out_degrees = {}
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)
avg_out_deg = graphtools.get_avg_out_degree_from_graph(graph)
avg_out_degrees[end_day] = avg_out_deg
print end_day, avg_out_deg
utils.save_node_map(avg_out_degrees, ("avg_out_degrees"))
def frac_nodes_in_gcc_over_time():
frac_nodes_in_gcc = {}
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)
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 effective_diameter_over_time():
effective_diameters = {}
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 user_sell_frequency(g, stamp=''):
"""Same as above, but considers in degree, or when the node
is payed, ie sells something in exchange for bitcoins.
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = g.in_degree(node)
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_sell_freq_'+stamp)
def user_sell_frequency(g, stamp=''):
"""Same as above, but considers in degree, or when the node
is payed, ie sells something in exchange for bitcoins.
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = g.in_degree(node)
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_sell_freq_' + stamp)
def user_buy_frequency(g, stamp=''):
"""Same as first method, but now for the frequency in which a user spends
money, ie out degree
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = g.out_degree(node)
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_buy_freq_' + stamp)
utils.save_node_map(frequency, 'freq_to_buy_' + stamp)
def user_buy_frequency(g, stamp=''):
"""Same as first method, but now for the frequency in which a user spends
money, ie out degree
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = g.out_degree(node)
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_buy_freq_'+stamp)
utils.save_node_map(frequency, 'freq_to_buy_'+stamp)
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 user_transaction_frequency(g, stamp=''):
""" Takes in a graph representing a snippet of the bitcoin network
and graphs a representation of each user's transaction frequency.
Creates two dicts: one with a frequency mapped to the number of nodes
with that amount. The other maps a specific node to its frequency.
The stamp should be something to append to the outputed csv so we know
which graph snippet the data refers to.
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = len(g.neighbors(node))
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_transaction_freq_' + stamp)
utils.save_node_map(frequency, 'freq_to_transaction_' + stamp)
# plot_node_data.plot_node_map(node_to_freq)
return frequency
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 user_transaction_frequency(g, stamp=''):
""" Takes in a graph representing a snippet of the bitcoin network
and graphs a representation of each user's transaction frequency.
Creates two dicts: one with a frequency mapped to the number of nodes
with that amount. The other maps a specific node to its frequency.
The stamp should be something to append to the outputed csv so we know
which graph snippet the data refers to.
"""
frequency = {}
node_to_freq = {}
for node in g.nodes():
freq = len(g.neighbors(node))
frequency[freq] = frequency[freq] + 1 if frequency.get(freq) else 1
node_to_freq[node] = freq
utils.save_node_map(node_to_freq, 'node_transaction_freq_'+stamp)
utils.save_node_map(frequency, 'freq_to_transaction_'+stamp)
# plot_node_data.plot_node_map(node_to_freq)
return frequency
def user_transaction_amount(g, stamp=''):
"""Takes in a graph representing a snippet of the bitcoin network
and graphs a representation of the amount of bitcoins passing through
a user. Also maps the frequency of that amount by rounding amounts
in a specific digit position.
TODO: decide if it should be spent, recieved, all, or some combination
"""
amount_frequency = {}
node_to_amount = {}
for node in g.nodes():
total = 0.0
for s, e, d in g.edges(node, data=True):
total += d['value']
node_to_amount[node] = total
rounded = int(total * _ROUND_FACTOR) / float(_ROUND_FACTOR)
amount_frequency[rounded] = amount_frequency[rounded] + 1 if \
amount_frequency.get(rounded) else 1
utils.save_node_map(node_to_amount, 'node_transaction_amount_'+stamp)
utils.save_node_map(amount_frequency, 'amount_to_frequency_'+stamp)
def user_transaction_amount(g, stamp=''):
"""Takes in a graph representing a snippet of the bitcoin network
and graphs a representation of the amount of bitcoins passing through
a user. Also maps the frequency of that amount by rounding amounts
in a specific digit position.
TODO: decide if it should be spent, recieved, all, or some combination
"""
amount_frequency = {}
node_to_amount = {}
for node in g.nodes():
total = 0.0
for s, e, d in g.edges(node, data=True):
total += d['value']
node_to_amount[node] = total
rounded = int(total * _ROUND_FACTOR) / float(_ROUND_FACTOR)
amount_frequency[rounded] = amount_frequency[rounded] + 1 if \
amount_frequency.get(rounded) else 1
utils.save_node_map(node_to_amount, 'node_transaction_amount_' + stamp)
utils.save_node_map(amount_frequency, 'amount_to_frequency_' + stamp)
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"))
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"))
