def graph_measures(graph: gt.Graph) -> pd.DataFrame:
def get_attrs(attrs):
return (attrs[1][0], attrs[1][1][1], attrs[0])
def append_val(key, prop, v):
measures[key][0].append(prop[v])
_, vp_authority, vp_hub = gt.hits(graph)
measures = {
key: ([], prop)
for key, prop in {
'tp_group': graph.vp.group_name,
'tp_author': graph.vp.username,
'tn_degree_in': graph.degree_property_map('in'),
'tn_degree_out': graph.degree_property_map('out'),
'tn_degree_total': graph.degree_property_map('total'),
'tn_pagerank': gt.pagerank(graph),
'tn_betweenness': gt.betweenness(graph)[0],
'tn_closeness': gt.closeness(graph),
'tn_eigenvector': gt.eigenvector(graph)[1],
'tn_authority': vp_authority,
'tn_hub': vp_hub,
'tn_lcc': gt.local_clustering(graph)
}.items()
}
for attrs in product(graph.vertices(), measures.items()):
append_val(*get_attrs(attrs))
return pd.DataFrame(
dict(map(lambda item: (item[0], item[1][0]),
measures.items()))).fillna(0)
def clusteringStats(g):
clustring_vertices = local_clustering(g).a
print("Clusterização")
stats(clustring_vertices)
print("\tGlobal", global_clustering(g)[0])
histogram(np.histogram(clustring_vertices),
"Distribuição de clusterização", "$C_i$", "$C$",
sys.argv[1][:-8] + ".clusterizacao")
def sredni_wspolczynnik_klasteryzacji(self):
'''The local clustering coefficient [watts-collective-1998] ci is defined as
ci=|{ejk}| / (ki(ki−1)) :vj,vk∈Ni,ejk∈E
where ki is the out-degree of vertex i, and
Ni={vj:eij∈E}
is the set of out-neighbours of vertex i.
For undirected graphs the value of ci is normalized as c′i=2ci.
The implemented algorithm runs in O(|V|⟨k⟩2) time, where ⟨k⟩ is the average out-degree
https://en.wikipedia.org/wiki/Clustering_coefficient'''
lc = local_clustering(self.graph, undirected=True)
return vertex_average(self.graph, lc)[0]
def sredni_wspolczynnik_klasteryzacji(self):
'''The local clustering coefficient [watts-collective-1998] ci is defined as
ci=|{ejk}| / (ki(ki−1)) :vj,vk∈Ni,ejk∈E
where ki is the out-degree of vertex i, and
Ni={vj:eij∈E}
is the set of out-neighbours of vertex i.
For undirected graphs the value of ci is normalized as c′i=2ci.
The implemented algorithm runs in O(|V|⟨k⟩2) time, where ⟨k⟩ is the average out-degree
https://en.wikipedia.org/wiki/Clustering_coefficient'''
lc = local_clustering(self.graph, undirected=True)
return vertex_average(self.graph, lc)[0]
def cohesion_index(graph, seed):
g = graph.copy()
filt = graph.new_vertex_property('bool')
for vertex in seed:
filt[vertex] = True
g.set_vertex_filter(filt)
g.purge_vertices()
clust = gt.local_clustering(g)
ic = np.mean(clust.a)
return ic
def compute_and_save_global_clustering(g, filename):
clust = gt.local_clustering(g, undirected=False)[0]
add_property(g, "clustering", "double", clust)
g.save(filename)
#############
closeness = GT.closeness(GT.extract_largest_component(g), norm=False, harmonic=True).get_array()
plt.title("Closeness distribution")
plt.ylabel('#Nodes')
plt.xlabel('Closeness coefficient')
plt.bar(*float_distribution(closeness, 40), width=(max(closeness)-min(closeness))/50)
plt.savefig(f"img/closeness_dist.png", format='png')
plt.close()
print(f"top {TOP} closeness nodes: {get_top(closeness, TOP)}")
del closeness
##############
# Clustering #
##############
clustering = list(GT.local_clustering(g))
plt.title("Clustering distribution")
plt.ylabel('#Nodes')
plt.xlabel('Local clustering coefficient')
plt.bar(*float_distribution(clustering, 40), width=(max(clustering)-min(clustering))/50)
plt.savefig(f"img/clustering_dist.png", format='png')
plt.close()
print(f"top {TOP} local clustering nodes: {get_top(clustering, TOP)}")
del clustering
draw(g, 'deg', output='network.pdf', vertex_size=5, edge_pen_width=1, output_size=(1000,1000))
def lcl_clus_coef_dist(g):
return gt.local_clustering(g).get_array()
def lcl_clus_coef(g):
return gt_stats.vertex_average(g, gt.local_clustering(g))[0]
CoocEdgeWeight = gtgCooc.edge_properties['weight']
CoocVertexId = gtgCooc.vertex_properties['id']
CoocVertexIter = gtgCooc.vertices()
print("4")
CoocBetween, ep = gt.betweenness(gtgCooc, weight=CoocEdgeWeight, norm=True)
print("5")
#ee, CoocEigen = gt.eigenvector(gtgCooc, weight=CoocEdgeWeight)
print("6")
#ee, CoocAuthority, CoocHub = gt.hits(gtgCooc, weight =CoocEdgeWeight)
#CoocPagerank = gt.pagerank(gtgCooc, weight =CoocEdgeWeight)
CoocCloseness = gt.closeness(gtgCooc, weight=CoocEdgeWeight)
print("7")
CoocKatz = gt.katz(gtgCooc, weight=CoocEdgeWeight)
CoocClustering = gt.local_clustering(gtgCooc)
print("8")
CoocDegree = gtgCooc.degree_property_map("total", weight=CoocEdgeWeight)
print("9")
print("where")
print("A")
print len(nodeList)
print("B")
tempCoocList = []
for i in CoocVertexIter:
temp = (str(j_id), CoocVertexId[i], CoocDegree[i], CoocBetween[i],
CoocCloseness[i], CoocKatz[i], CoocKatz[i], CoocClustering[i],
"3")
temp = [