'font.family' : 'serif',

'font.serif' : ['Times New Roman'],

'font.size' : 7

# wikipedia graph structural statistics

print 'before load'

network = load_graph("output/wikipedianetwork.xml.gz")

print 'after load'

out_hist = vertex_hist(network, "out")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(out_hist[1][:-1], out_hist[0], marker='o')

plt.xlabel('Out-degree')

plt.ylabel('Frequency')

plt.gca().set_ylim([1, 10**6])

#plt.title('Out-degree Distribution')

plt.tight_layout()

plt.savefig('output/wikipedia-out-deg-dist.pdf')

plt.clf()

in_hist = vertex_hist(network, "in")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(in_hist[1][:-1], in_hist[0], marker='o')

plt.xlabel('In-degree')

plt.ylabel('Frequency')

plt.gca().set_ylim([1, 10**6])

#plt.title('In-degree Distribution')

plt.tight_layout()

plt.savefig('output/wikipedia-in-deg-dist.pdf')

plt.clf()

total_hist = vertex_hist(network, "total")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(total_hist[1][:-1], total_hist[0], marker='o')

plt.xlabel('Degree')

plt.ylabel('Frequency')

plt.gca().set_ylim([1, 10**6])

#plt.title('Degree Distribution')

plt.tight_layout()

plt.savefig('output/wikipedia-deg-dist.pdf')

plt.clf()

clust = network.vertex_properties["local_clust"]

#clust = local_clustering(network, undirected=False)

#hist, bin_edges = np.histogram(clust.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Local Clustering Coefficient C')

#plt.ylabel('P(x<=C)')

#plt.title('Clustering Coefficient Distribution')

#plt.savefig('output/wikipedia-clust-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(clust.get_array(), ax)

#ax.set_title('Clustering Coefficient Distribution')

ax.set_xlabel('Local Clustering Coefficient $C')

ax.set_ylabel('P(x<=C)')

ax.set_ylim([0, 1])

fig.tight_layout()

fig.savefig('output/wikipedia-clust-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(clust.get_array(), ax)

#ax.set_title('Clustering Coefficient Distribution')

ax.set_xlabel('Local Clustering Coefficient C')

ax.set_ylabel('P(x>=C)')

ax.set_ylim([10**-4, 10**-0.5])

fig.tight_layout()

fig.savefig('output/wikipedia-clust-ccdf.pdf')

plt.clf()

prank = network.vertex_properties["page_rank"]

#hist, bin_edges = np.histogram(prank.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Page rank Pr')

#plt.ylabel('P(x<=Pr)')

#plt.title('Page rank Distribution')

#plt.savefig('output/wikipedia-prank-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(prank.get_array(), ax)

#ax.set_title('Page Rank Distribution')

ax.set_xlabel('Page rank Pr')

ax.set_ylabel('P(x<=Pr)')

ax.set_ylim([0, 1])

fig.tight_layout()

fig.savefig('output/wikipedia-prank-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(prank.get_array(), ax)

#ax.set_title('Page Rank Distribution')

ax.set_xlabel('Page rank Pr')

ax.set_ylabel('P(x>=Pr)')

fig.tight_layout()

fig.savefig('output/wikipedia-prank-ccdf.pdf')

plt.clf()

kcore = network.vertex_properties["kcore"]

#hist, bin_edges = np.histogram(kcore.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Kcore kC')

#plt.ylabel('P(x<=kC)')

#plt.title('K-Core Distribution')

#plt.savefig('output/wikipedia-kcore-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(kcore.get_array(), ax)

#ax.set_title('K-Core Distribution')

ax.set_xlabel('k-Core kC')

ax.set_ylabel('P(x<=kC)')

ax.set_ylim([0, 1])

fig.tight_layout()

fig.savefig('output/wikipedia-kcore-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(kcore.get_array(), ax)

#ax.set_title('K-Core Distribution')

ax.set_xlabel('k-Core kC')

ax.set_ylabel('P(x>=kC)')

fig.tight_layout()

fig.savefig('output/wikipedia-kcore-ccdf.pdf')

plt.clf()

eigenvector_centr = network.vertex_properties["eigenvector_centr"]

#hist, bin_edges = np.histogram(eigenvector_centr.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Eigenvector Centrality E')

#plt.ylabel('P(x<=E)')

#plt.title('Eigenvector Centrality Distribution')

#plt.savefig('output/wikipedia-eigenvcentr-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(eigenvector_centr.get_array(), ax)

#ax.set_title('Eigenvector Centrality E')

ax.set_xlabel('Eigenvector Centrality E')

ax.set_ylabel('P(x<=E)')

ax.set_ylim([0, 1])

fig.tight_layout()

fig.savefig('output/wikipedia-eigenvcentr-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(eigenvector_centr.get_array(), ax)

#ax.set_title('Eigenvector Centrality E')

ax.set_xlabel('Eigenvector Centrality E')

ax.set_ylabel('P(x>=E)')

fig.tight_layout()

fig.savefig('output/wikipedia-eigenvcentr-ccdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X>=f)$')

ax.set_ylim([0, 1])

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-features-cdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','eigenvector_centr','page_rank', 'hub', 'authority', 'kcore']:

feature = network.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X<=f)$')

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-features-ccdf.pdf')

plt.clf()

# wikipedia transitions graph structural statistics

print 'before load'

network_transitions = load_graph("output/transitionsnetwork.xml.gz")

print 'after load'

out_hist = vertex_hist(network_transitions, "out")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(out_hist[1][:-1], out_hist[0], marker='o')

plt.xlabel('Out-degree')

plt.ylabel('Frequency')

plt.gca().set_ylim([1, 10**6])

#plt.title('Out-degree Distribution')

plt.savefig('output/wikipedia-transitions-out-deg-dist.pdf')

plt.clf()

in_hist = vertex_hist(network_transitions, "in")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(in_hist[1][:-1], in_hist[0], marker='o')

plt.xlabel('In-degree')

plt.ylabel('Frequency')

#plt.title('In-degree Distribution')

plt.gca().set_ylim([1, 10**6])

plt.savefig('output/wikipedia-transitions-in-deg-dist.pdf')

plt.clf()

total_hist = vertex_hist(network_transitions, "total")

plt.gca().set_yscale('log')

plt.gca().set_xscale('log')

plt.plot(total_hist[1][:-1], total_hist[0], marker='o')

plt.xlabel('Degree')

plt.ylabel('Frequency')

#plt.title('Degree Distribution')

plt.gca().set_ylim([1, 10**6])

plt.savefig('output/wikipedia-transitions-deg-dist.pdf')

plt.clf()

#clust = local_clustering(network_transitions, undirected=False)

clust = network_transitions.vertex_properties["local_clust"]

#hist, bin_edges = np.histogram(clust.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Local Clustering Coefficient C')

#plt.ylabel('P(x<=C)')

#plt.title('Clustering Coefficient Distribution')

#plt.savefig('output/wikipedia-transitions-clust-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(clust.get_array(), ax)

#ax.set_title('Clustering Coefficient Distribution')

ax.set_xlabel('Local Clustering Coefficient C')

ax.set_ylabel('P(x<=C)')

fig.savefig('output/wikipedia-transitions-clust-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(clust.get_array(), ax)

ax.set_title('Clustering Coefficient Distribution')

ax.set_xlabel('Local Clustering Coefficient C')

ax.set_ylabel('P(x>=C)')

fig.savefig('output/wikipedia-transitions-clust-ccdf.pdf')

plt.clf()

prank = network_transitions.vertex_properties["page_rank"]

#hist, bin_edges = np.histogram(prank.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Page rank Pr')

#plt.ylabel('P(x<=Pr)')

#plt.title('Page rank Distribution')

#plt.savefig('output/wikipedia-transitions-prank-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(prank.get_array(), ax)

#ax.set_title('Page Rank Distribution')

ax.set_xlabel('Page rank Pr')

ax.set_ylabel('P(x<=Pr)')

fig.savefig('output/wikipedia-transitions-prank-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(prank.get_array(), ax)

#ax.set_title('Page Rank Distribution')

ax.set_xlabel('Page rank Pr')

ax.set_ylabel('P(x>=Pr)')

fig.savefig('output/wikipedia-transitions-prank-ccdf.pdf')

plt.clf()

kcore = network_transitions.vertex_properties["kcore"]

#hist, bin_edges = np.histogram(kcore.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Kcore kC')

#plt.ylabel('P(x<=kC)')

#plt.title('K-Core Distribution')

#plt.savefig('output/wikipedia-transitions-kcore-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(kcore.get_array(), ax)

#ax.set_title('K-Core Distribution')

ax.set_xlabel('k-Core kC')

ax.set_ylabel('P(x<=kC)')

fig.savefig('output/wikipedia-transitions-kcore-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(kcore.get_array(), ax)

#ax.set_title('K-Core Distribution')

ax.set_xlabel('k-Core kC')

ax.set_ylabel('P(x>=kC)')

fig.savefig('output/wikipedia-transitions-kcore-ccdf.pdf')

plt.clf()

eigenvector_centr = network_transitions.vertex_properties["eigenvector_centr"]

#hist, bin_edges = np.histogram(eigenvector_centr.get_array(), 100, density=True)

#cdf = np.cumsum(hist)

#plt.plot(bin_edges[1:], cdf, marker='o')

#plt.xlabel('Eingenvector centrality E')

#plt.ylabel('P(x<=E)')

#plt.title('Eigenvector Centrality Distribution')

#plt.savefig('output/wikipedia-transitions-eigenvcentr-cdf.pdf')

fig, ax = plt.subplots()

powerlaw.plot_cdf(eigenvector_centr.get_array(), ax)

#ax.set_title('Eigenvector Centrality Distribution')

ax.set_xlabel('Eingenvector centrality E')

ax.set_ylabel('P(x<=E)')

fig.savefig('output/wikipedia-transitions-eigenvcentr-cdf.pdf')

plt.clf()

fig, ax = plt.subplots()

powerlaw.plot_ccdf(eigenvector_centr.get_array(), ax)

#ax.set_title('Eigenvector Centrality Distribution')

ax.set_xlabel('Eingenvector centrality E')

ax.set_ylabel('P(x>=E)')

fig.savefig('output/wikipedia-transitions-eigenvcentr-ccdf.pdf')

plt.clf()

print 'before hits'

#ee, authority, hub = hits(network_transitions)

#network_transitions.vertex_properties["authority"] = authority

#network_transitions.vertex_properties["hub"] = hub

#network_transitions.save("output/transitionsnetwork.xml.gz")

print 'after hits'

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network_transitions.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X>=f)$')

ax.set_ylim([0, 1])

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-transitions-features-cdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network_transitions.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X<=f)$')

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-transitions-features-ccdf.pdf')

# wikipedia graph structural statistics

print 'before load'

network = load_graph("output/wikipedianetwork.xml.gz")

print 'after load'

print 'before load'

network_transitions = load_graph("output/transitionsnetwork.xml.gz")

print 'after load'

out_hist = vertex_hist(network, "out")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipedia', color='b')

out_hist = vertex_hist(network_transitions, "out")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlabel('Out-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-outdegree.pdf')

out_hist = vertex_hist(network, "in")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipedia', color='b')

out_hist = vertex_hist(network_transitions, "in")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlabel('In-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-indegree.pdf')

out_hist = vertex_hist(network, "total")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipeida', color='b')

out_hist = vertex_hist(network_transitions, "total")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlabel('Degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

# wikipedia graph structural statistics

print 'before load'

network = load_graph("output/wikipedianetwork.xml.gz")

print 'after load'

print 'before load'

network_transitions = load_graph("output/transitionsnetwork.xml.gz")

print 'after load'

network = GraphView(network, vfilt=filter_transitions(network,network_transitions))

print 'filter out'

out_hist = vertex_hist(network, "out")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipedia', color='b')

out_hist = vertex_hist(network_transitions, "out")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**6])

ax.set_xlabel('Out-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-outdegree-filtered.pdf')

out_hist = vertex_hist(network, "in")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipedia', color='b')

out_hist = vertex_hist(network_transitions, "in")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**6])

ax.set_xlabel('In-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-indegree-filtered.pdf')

out_hist = vertex_hist(network, "total")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='wikipeida', color='b')

out_hist = vertex_hist(network_transitions, "total")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, label='transitions', color='r')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':4}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**6])

ax.set_xlabel('Degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-degree-filtered.pdf')

print 'fit'

fp.powerLawExponent('transitions', network_transitions)

filter = network1.new_vertex_property('bool')

print 'filter'

found_false = False

for i,v in enumerate(network1.vertices()):

if i% 100000==0:

print i

if v in network2.vertices():

#print "true"

filter[v]=True

else:

if not found_false:

print "false"

found_false=True

filter[v]=False

print 'before load'

network = load_graph("output/wikipedianetwork.xml.gz")

print 'after load'

print 'before load'

network_transitions = load_graph("output/transitionsnetwork.xml.gz")

print 'after load'

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network_transitions.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X>=f)$')

ax.set_ylim([0, 1])

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-transitions-features-cdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network_transitions.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X<=f)$')

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-transitions-features-ccdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','page_rank', 'hub', 'authority', 'kcore']:

feature = network.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X>=f)$')

ax.set_ylim([0, 1])

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

plt.savefig('output/wikipedia-features-cdf.pdf')

plt.clf()

colors= {'local_clust':'r','eigenvector_centr':'b', 'page_rank': 'g', 'kcore':'m', 'hub': 'c', 'authority':'k'}

labels = {'local_clust': 'clust.', 'eigenvector_centr':'eigen. centr.','page_rank': 'page rank', 'kcore': 'kcore', 'hub':'hub', 'authority':'authority'}

fig = plt.figure()

ax = fig.add_subplot(111)

for f in ['local_clust','eigenvector_centr','page_rank', 'hub', 'authority', 'kcore']:

feature = network.vertex_properties[f]

powerlaw.plot_cdf(feature.get_array(), ax, label=labels[f],color=colors[f])

ax.set_xlabel('Feature $f$')

ax.set_ylabel('$P(X<=f)$')

plt.legend(fancybox=True, loc=3, ncol=2, prop={'size':4})

plt.tight_layout()

print 'before select'

db = MySQLDatabase(DATABASE_HOST, DATABASE_USER, DATABASE_PASSWORD, DATABASE_NAME)

conn = db._create_connection()

cursor = conn.cursor()

cursor.execute('SELECT source_article_id, target_article_id FROM link_occurences where source_article_id in '

' (select distinct prev_id from clickstream_derived_internal_links);')

result = cursor.fetchall()

network = Graph()

print 'after select'

print 'result len'

print len(result)

for i, link in enumerate(result):

if i % 1000000==0:

print i, len(result)

network.add_edge(link[0], link[1])

# filter all nodes that have no edges

print 'filter nodes with degree zero graph tool specific code'

network = GraphView(network, vfilt=lambda v : v.out_degree()+v.in_degree()>0 )

print 'before save'

network.save("output/wikipedianetworkfilteredwithtransitions_prev_id.xml.gz")

print 'done'

cursor.execute('SELECT source_article_id, target_article_id FROM link_occurences where target_article_id in '

' (select distinct curr_id from clickstream_derived_internal_links);')

result = cursor.fetchall()

network = Graph()

print 'after select'

print 'resutl len'

print len(result)

for i, link in enumerate(result):

if i % 1000000==0:

print i, len(result)

network.add_edge(link[0], link[1])

# filter all nodes that have no edges

print 'filter nodes with degree zero graph tool specific code'

network = GraphView(network, vfilt=lambda v : v.out_degree()+v.in_degree()>0 )

print 'before save'

network.save("output/wikipedianetworkfilteredwithtransitions_curr_id.xml.gz")

# wikipedia graph structural statistics

print 'before load'

network = load_graph("output/wikipedianetworkfilteredwithtransitions_"+network_name+".xml.gz")

print 'after load'

print 'before load'

network_transitions = load_graph("output/transitionsnetwork.xml.gz")

print 'after load'

out_hist = vertex_hist(network, "out")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label=r'$D_{wiki}$')

out_hist = vertex_hist(network_transitions, "out")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label=r'$D_{trans}$')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5})

#plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**4])

ax.set_xlabel('Out-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-outdegree-filtered'+network_name+'-sql.pdf', bbox_inches='tight')

out_hist = vertex_hist(network, "in")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label=r'$D_{wiki}$')

out_hist = vertex_hist(network_transitions, "in")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label=r'$D_{trans}$')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5})

#plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**6])

ax.set_xlabel('In-degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-indegree-filtered'+network_name+'-sql.pdf', bbox_inches='tight')

out_hist = vertex_hist(network, "total")

fig, ax = plt.subplots()

ax.set_yscale('log')

ax.set_xscale('log')

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label='wikipeida')

out_hist = vertex_hist(network_transitions, "total")

ax.plot(out_hist[1][:-1], out_hist[0], marker='o', markersize=3, markeredgecolor='none', label='transitions')

plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5})

#plt.legend(fancybox=True, loc='upper right', ncol=1, prop={'size':5}, numpoints=1, handlelength=0)

ax.set_ylim([10**0, 10**6])

ax.set_xlim([10**0, 10**6])

ax.set_xlabel('Degree')

ax.set_ylabel('Frequency')

fig.tight_layout()

fig.savefig('output/wikipedia-transitions-degree-filtered'+network_name+'-sql.pdf', bbox_inches='tight')

print 'fit'+network_name

fp.powerLawExponent('transitions', network_transitions)