def nodes_vs_edges_over_time():
num_nodes = []
num_edges = []
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 = []
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 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"))
""" The purpose of this file is to aggregate data from the bitcoin network
over time
"""
import sys
import utils
import networkx as nx
import graphgen
import graphtools
import tags_over_time
import forest_fire
from networkx.algorithms import *
from model import *
'''
_HMS = 1000000
slices = graphgen.generate_weighted_time_slices()
i = 0
dates = []
avg_clust = []
lccs = []
largest_scc = []
dates = []
avg_clust_p = []
lccs_p = []
for start, end in slices:
g = graphgen.get_graph_slice(start * _HMS, end * _HMS)
if len(g) == 0:
continue
rg = nx.gnm_random_graph(g.number_of_nodes(), g.number_of_edges(), directed=True)
pfg = nx.barabasi_albert_graph(g.number_of_nodes(), g.number_of_edges()/g.number_of_nodes())
stamp = str(start) + '_' + str(end)
lccs = []
largest_scc = []
for start, end in slices:
g = graphgen.get_graph_slice(start * _HMS, end * _HMS)
if len(g) == 0:
continue
# stamp = str(start) + '_' + str(end)
#tags_over_time.user_transaction_frequency(g, stamp)
# tags_over_time.user_transaction_amount(g, stamp)
# tags_over_time.user_buy_frequency(g, stamp)
# tags_over_time.user_sell_frequency(g, stamp)
# d = assortativity.average_degree_connectivity(g)
# utils.save_node_map(d, stamp)
deg_connectivity.append(assortativity.average_degree_connectivity(g))
undir_g = nx.Graph(g.copy())
avg_clust.append(nx.average_clustering(undir_g))
lccs.append(len(graphtools.get_lcc_from_graph(g)))
largest_scc.append(len(graphtools.get_sccs_from_graph(g)[0]))
dates.append(start) # hack for now to save time...
print 'finished %s tag over time' % i
i += 1
utils.save_lists(dates, deg_connectivity, stamp='deg_conn')
utils.save_lists(dates, avg_clust, stamp='avg_clust')
utils.save_lists(dates, lccs, stamp='lccs')
utils.save_lists(dates, largest_scc, stamp='largest_scc')
# plot single values over time
# Gini coefficient (way to get single number from distribution)
# stamp = str(start) + '_' + str(end) #tags_over_time.user_transaction_frequency(g, stamp) # tags_over_time.user_transaction_amount(g, stamp) # tags_over_time.user_buy_frequency(g, stamp) # tags_over_time.user_sell_frequency(g, stamp) # d = assortativity.average_degree_connectivity(g) # utils.save_node_map(d, stamp) deg_connectivity.append(assortativity.average_degree_connectivity(g)) undir_g = nx.Graph(g.copy()) avg_clust.append(nx.average_clustering(undir_g)) lccs.append(len(graphtools.get_lcc_from_graph(g))) largest_scc.append(len(graphtools.get_sccs_from_graph(g)[0])) dates.append(start) # hack for now to save time... print 'finished %s tag over time' % i i += 1 utils.save_lists(dates, deg_connectivity, stamp='deg_conn') utils.save_lists(dates, avg_clust, stamp='avg_clust') utils.save_lists(dates, lccs, stamp='lccs') utils.save_lists(dates, largest_scc, stamp='largest_scc') # plot single values over time # Gini coefficient (way to get single number from distribution)
