def deg_powerlaw_low_high_sat(g):
pl_fit = pl.Fit(
gt_stats.vertex_hist(g, 'total')[0] / g.num_vertices(),
verbose=False
)
return (
pl_fit.alpha,
pl_fit.xmin,
pl_fit.xmax
)
def plot_centralities(network, title="Centrality measures"):
g = network.graph
comm_size = g.num_vertices()
closeness = centrality.closeness(g).get_array().tolist()
max_eigenval, eigenvec = centrality.eigenvector(g)
# eigenvector = [x/max_eigenval for x in eigenvec.get_array().tolist()] #normalize!
eigenvector = eigenvec.get_array().tolist()
betw, _edges = centrality.betweenness(g, norm=True)
betweenness = betw.get_array().tolist()
P.suptitle(title)
# P.figure()
print "nans", len([x for x in closeness if isnan(x)])
closeness = [0 if isnan(x) else x for x in closeness]
# closeness = [x for x in closeness if not isnan(x)]
closeness = _filtered(closeness, comm_size)
print "closeness", closeness
print "non zeros", len([x for x in closeness if x != 0])
P.subplot(2, 2, 1)
plot_hist(closeness)
P.xlabel("Closeness centrality")
P.ylabel("Number of nodes (total={})".format(len(closeness)))
counts, degrees = stats.vertex_hist(g, "in", float_count=False)
print "counts : ", len(counts), counts
print "degrees: ", len(degrees), degrees
counts = list(counts)
counts.append(0)
P.subplot(2, 2, 2)
P.bar(degrees, counts, align='center', color="#348ABD")
# P.hist(counts, bins=degrees, )
P.xlabel("Degree centrality (in)")
P.ylabel("Number of nodes (total={})".format(sum(counts)))
P.xlim(0, max(degrees))
betweenness = _filtered(betweenness, comm_size)
print "betweenness", betweenness
P.subplot(2, 2, 3)
plot_hist(betweenness)
P.xlabel("Betweenness centrality")
P.ylabel("Number of nodes (total={})".format(len(betweenness)))
eigenvector = _filtered(eigenvector, comm_size)
print "eigenvector", eigenvector
P.subplot(2, 2, 4)
plot_hist(eigenvector)
P.xlabel("Eigenvector centrality")
P.ylabel("Number of nodes (total={})".format(len(eigenvector)))
P.show()
def cum_deg_powerlaw_low_high_sat(g):
deg_hist = gt_stats.vertex_hist(g, 'total')[0] / g.num_vertices()
pl_fit = pl.Fit(
np.flip(np.flip(deg_hist, 0).cumsum(), 0),
verbose=False
)
return (
pl_fit.alpha,
pl_fit.xmin,
pl_fit.xmax
)
def user_network_summary(g):
span = "{:D MMM YYYY, HH:mm} - {:D MMM YYYY, HH:mm}".format(
arrow.get(g.edge_properties["created_at"].a.min()),
arrow.get(g.edge_properties["created_at"].a.max())
)
largest_component = label_largest_component(g, directed=False).a.sum()
display(Markdown("### " + g.graph_properties["track"].replace("#", r"\#")))
display(Markdown("#### " + span))
graph_draw(g, inline=True, output_size=[1000, 1000],
vertex_fill_color=[.2, .3, .9, .7], vertex_size=2)
stats = pd.DataFrame([
["Vertices",
g.num_vertices()],
["Edges",
g.num_edges()],
["Avg. degree",
float(g.num_edges()) / g.num_vertices()],
["Avg. clustering",
vertex_average(g, local_clustering(g))[0]],
["Giant component share",
"{:.1%}".format(largest_component / g.num_vertices())]
], columns=["Metric", "Value"])
display(stats)
bins = 20
counts, _ = vertex_hist(g, "in", range(bins))
plt.bar(range(1, bins), counts, align="center")
plt.xticks(range(bins))
plt.xlim([0.5, bins - 1])
plt.title("Degree distribution")
plt.show()
def get_descriptors(network, short_name, nx_network, already_calculated=False):
def _prefixToTitle(prefix):
if prefix == 'a':
return "Artists"
elif prefix == 't':
return "Tags"
elif prefix == 'u':
return 'Users'
filename = "cache/{}.pickle".format(short_name)
if os.path.isfile(filename):
result = pickle.load(open(filename, 'rb'))
return result
result = {}
prefix1, prefix2 = short_name[0], short_name[1]
t1 = _prefixToTitle(prefix1)
t2 = _prefixToTitle(prefix2)
result['name'] = short_name
result['title_dd1'] = PLOT_TITLES[short_name].format(t1, "")
result['title_dd2'] = PLOT_TITLES[short_name].format(t2, "")
result['title_dd1_acum'] = PLOT_TITLES[short_name].format(
t1, " Cumulative")
result['title_dd2_acum'] = PLOT_TITLES[short_name].format(
t2, " Cumulative")
result['title_wd'] = PLOT_TITLES['wd'].format("", t1, t2)
result['title_wd_acum'] = PLOT_TITLES['wd'].format("Cumulative ", t1, t2)
result['title_cd'] = PLOT_TITLES['cd'].format(t1, t2)
result['title_sp'] = PLOT_TITLES['sp'].format(t1, t2)
result['filename_dd'] = '{}_dd'.format(short_name) # degree input dist
result['filename_ddl'] = '{}_dd_log'.format(
short_name) # degree dist (log)
result['filename_dd1'] = '{}_{}_dd'.format(short_name[0],
short_name) # degree input dist
result['filename_dd2'] = '{}_{}_dd'.format(short_name[1],
short_name) # degree input dist
result['filename_dd1l'] = '{}_{}_dd_log'.format(
short_name[0], short_name) # degree input dist
result['filename_dd2l'] = '{}_{}_dd_log'.format(
short_name[1], short_name) # degree input dist
result['filename_dd1_acum'] = '{}_{}_dd_acum'.format(
short_name[0], short_name) # degree input dist
result['filename_dd2_acum'] = '{}_{}_dd_acum'.format(
short_name[1], short_name) # degree input dist
result['filename_wd'] = '{}_wd'.format(short_name) # weight distribution
result['filename_wdl'] = '{}_wd_log'.format(
short_name) # weight distribution
result['filename_wd_acum'] = '{}_wd_acum'.format(
short_name) # weight distribution
result['filename_sp'] = '{}_sp'.format(short_name) # shortest path
result['filename_cd'] = '{}_cd'.format(short_name) # components
result['filename_cdl'] = '{}_cd_log'.format(short_name) #
nodes = network.get_vertices()
edges = network.get_edges()
result['num_nodes'] = {}
result['num_nodes']['total'] = nodes.shape[0]
result['num_edges'] = edges.shape[0]
result['degree'] = {"total": {}, "prefix1": {}, "prefix2": {}}
result['degree']["total"]['max'] = network.get_out_degrees(nodes).max()
result['degree']["total"]['min'] = network.get_out_degrees(nodes).min()
result['degree']["total"]['avg'] = network.get_out_degrees(nodes).mean()
result['degree']["total"]["counts"], result['degree']["total"][
"bins"] = st.vertex_hist(network, "out")
nodes1, nodes2 = [], []
for node in nodes:
if prefix1 in network.vp['id'][node]:
nodes1.append(node)
elif prefix2 in network.vp['id'][node]:
nodes2.append(node)
result['num_nodes']['prefix1'] = len(nodes1)
result['degree']["prefix1"]['max'] = network.get_out_degrees(nodes1).max()
result['degree']["prefix1"]['min'] = network.get_out_degrees(nodes1).min()
result['degree']["prefix1"]['avg'] = network.get_out_degrees(nodes1).mean()
result['degree']["prefix1"]["counts"], result['degree']["prefix1"][
"bins"] = np.histogram(
network.get_out_degrees(nodes1),
bins=15) # result['degree']["total"]["bins"].shape[0]
result['degree']["prefix1"]["d"] = network.get_out_degrees(
nodes1) # result['degree']["total"]["bins"].shape[0]
if prefix1 == prefix2:
nodes2 = nodes1
result['num_nodes']['prefix2'] = len(nodes2)
result['degree']["prefix2"]['max'] = network.get_out_degrees(nodes2).max()
result['degree']["prefix2"]['min'] = network.get_out_degrees(nodes2).min()
result['degree']["prefix2"]['avg'] = network.get_out_degrees(nodes2).mean()
result['degree']["prefix2"]["counts"], result['degree']["prefix2"][
"bins"] = np.histogram(network.get_out_degrees(nodes2), bins=15)
result['degree']["prefix2"]["d"] = network.get_out_degrees(
nodes2) # result['degree']["total"]["bins"].shape[0]
result['weights'] = {}
weights = []
for v1, v2 in nx_network.edges():
weight = nx_network.get_edge_data(v1, v2)['weight']
weights.append(weight)
# result['weights']['counts'], result['weights']['bins'] = np.histogram(weights, bins=8)
result['weights']['d'] = weights
# estimated diamater and longest path
d, (v1, v2) = top.pseudo_diameter(network)
result['diameter'] = d
d_path = "{}-{}".format(network.vp['id'][v1], network.vp['id'][v2])
result['diameter_path'] = d_path
result['clustering'] = clu.global_clustering(network)
if not already_calculated:
net2 = gt.Graph(network) # undirected version
net2.set_directed(False)
result['sp'] = {}
result['sp']['counts'], result['sp']['bins'] = shortest_paths(net2)
# connected components
_, c2 = top.label_components(net2)
result['components'] = {}
result['components']['num'] = len(c2)
result['components']['bins'] = range(len(c2))
result['components']['counts'] = c2
pickle.dump(result, open(filename, "wb"))
return result
def avgSquares(graph):
counts, degrees = vertex_hist(graph, 'out')
return np.sum([(degrees[i]**2) * counts[i]
for i in range(len(counts))]) / graph.num_vertices()
def variance(g):
degree_hist = gt_stats.vertex_hist(g, 'total')[0] / g.num_vertices()
second_m = np.sum(degree_hist * (np.arange(len(degree_hist)) ** 2))
return second_m - avg_degree(g) ** 2
def max_degree(g):
return gt_stats.vertex_hist(g, 'total')[1][-2]
def kcore(g):
return gt_stats.vertex_hist(g, gt.kcore_decomposition(g))[0]
