def main():
for path in files:
print(path)
B = nx.Graph()
G = nx.read_gml('graphs/' + path)
B.add_nodes_from(relevant_subreddits, bipartite=0)
nodeList = set([n for n in G.nodes()])
for reddit in relevant_subreddits:
if reddit in nodeList:
nodeList.remove(reddit)
B.add_nodes_from(list(nodeList), bipartite=1)
# print(nx.number_of_nodes(G))
# B.add_edges_from([e for e in G.edges()])
edgeList = []
for e in G.edges():
if e[0] not in relevant_subreddits and e[
1] not in relevant_subreddits:
continue
edgeList.append(e)
B.add_edges_from(edgeList)
# print(nx.number_of_edges(G))
# print(bipartite.is_bipartite(B))
# print(bipartite.average_clustering(B))
result = random.sample(list(nodeList), 500)
print('Clustering Coefficient')
print(bipartite.average_clustering(B, result))
print('Density')
print(bipartite.density(B, result))
def graph_metrics(m): ''' Input: internal representation, n by L Return: 2-tuple - clustering coefficients of a bipartite graph built from m, a measure of local density of the connectivity ref: https://networkx.org/documentation/stable//reference/algorithms/generated/networkx.algorithms.bipartite.cluster.clustering.html#networkx.algorithms.bipartite.cluster.clustering - modularity: relative density of edges inside communities with respect to edges outside communities. ref: https://python-louvain.readthedocs.io/en/latest/api.html#community.modularity ''' sM = scipy.sparse.csr_matrix(m) G = bipartite.matrix.from_biadjacency_matrix(sM) avg_c = bipartite.average_clustering(G, mode="dot") partition = community_louvain.best_partition(G) modularity = community.modularity(partition, G) return avg_c, modularity
print("sa")
B.add_edges_from(edges)
print("sa")
bi0 = list(bi0)
bi1 = list(bi1)
random.seed()
for i in range((int)(percentage / 100 * len(bi0))):
r = random.randint(1, len(bi0) - 1)
print(i)
if bi0[r] not in bi0_sample:
bi0_sample.append(bi0[r])
random.seed()
for i in range((int)(percentage / 100 * len(bi1))):
r = random.randint(1, len(bi1) - 1)
print(i)
if bi1[r] not in bi1_sample:
bi1_sample.append(bi1[r])
sample_node = sum([bi0_sample, bi1_sample], [])
print("salam")
g = B.subgraph(sample_node)
return g
data = convert_txt_to_numpy('/Users/amirhossein/Desktop/term8/cpmplex/projects/HW2/out.actor-movie')
g=create_geaph(data,20)
a=bipartite.average_clustering(g)
print(a)
color_dict = {'0':'b','1':'r'}
#print(my_graph.nodes(data=True)) # which includes the category data
'''color_list =[] #]np.empty([num_segments+num_runs])
i=0
for node in my_graph.nodes(data=True):
if node[1]['cat']=='segment':
color_list.append('r')
elif node[1]['cat']=='date':
color_list.append('b') #node[1]['ncolor'])
i+=1'''
# let's look at some numberssss
#X, Y = bipartite.sets(my_graph)
print("The overall clustering coefficient is", bipartite.average_clustering(my_graph))
print("The clustering coefficient of the segment nodes is", bipartite.average_clustering(my_graph, segment_list))
print("The clustering coefficient of the dates is", bipartite.average_clustering(my_graph, dates[0:num_runs]))
print("The clustering coefficient for each connected component is")
for g in nx.connected_component_subgraphs(my_graph):
print(bipartite.average_clustering(g))
'''nodes = g.nodes(data=True)
print(type(nodes))
these_nodes = [node for node in nodes if node[1]['cat']=='date']
print("Date avg clust coeff is ", bipartite.average_clustering(g, these_nodes))
those_nodes = [node for node in nodes if node[1]['cat']=='segment']
print(bipartite.average_clustering(g, those_nodes)) '''
# size of the largest component
print("The size of the largest connected components is", len(nx.node_connected_component(my_graph, 'Sheridan Road Climb')))
def test_average_path_graph():
G = nx.path_graph(4)
assert_equal(bipartite.average_clustering(G, mode='dot'), 0.5)
assert_equal(bipartite.average_clustering(G, mode='max'), 0.5)
assert_equal(bipartite.average_clustering(G, mode='min'), 1)
def test_average_path_graph():
G = nx.path_graph(4)
assert_equal(bipartite.average_clustering(G, mode='dot'), 0.5)
assert_equal(bipartite.average_clustering(G, mode='max'), 0.5)
assert_equal(bipartite.average_clustering(G, mode='min'), 1)
print ("The 10 percentile is ", np.percentile(List_consignees_degrees,10))
print ("The 20 percentile is ", np.percentile(List_consignees_degrees,20))
print ("The 25 percentile is ", np.percentile(List_consignees_degrees,25))
print ("The 90 percentile is ", np.percentile(List_consignees_degrees,90))
print ("The 80 percentile is ", np.percentile(List_consignees_degrees,80))
print ("The 75 percentile is ", np.percentile(List_consignees_degrees,75))
print ("The 10 percentile is s", np.percentile(List_shippers_degrees,10))
print ("The 20 percentile is s", np.percentile(List_shippers_degrees,20))
print ("The 25 percentile is s", np.percentile(List_shippers_degrees,25))
print ("The 90 percentile is s", np.percentile(List_shippers_degrees,90))
print ("The 80 percentile is s", np.percentile(List_shippers_degrees,80))
print ("The 75 percentile is s", np.percentile(List_shippers_degrees,75))
print ("The number of connected components is", nx.number_connected_components(G))
print ("The ratio of shippers by consignees is", (nx.number_of_nodes(G)-len(List_consignees_degrees))/len(List_consignees_degrees))
print ("The ratio of edges by nodes is", nx.number_of_edges(G)/nx.number_of_nodes(G))
print ("The average bipartie clustering coefficient is", bipartite.average_clustering(G))
print ("The number of consignees working with one shipper is", nb_consignees_degree_one(G))
print ("The size of the max connected component is", nx.number_of_nodes(giant))
workbook = xlsxwriter.Workbook('summary.xlsx')
worksheet = workbook.add_worksheet()
worksheet.write(0,1, "Number of nodes")
worksheet.write(1,1, nx.number_of_nodes(G))
worksheet.write(0,2, "Number of consignees")
worksheet.write(1,2, len(List_consignees_degrees))
worksheet.write(0,3, "Number of shippers")
worksheet.write(1,3, nx.number_of_nodes(G)-len(List_consignees_degrees))
worksheet.write(0,4, "Number of edges")
worksheet.write(1,4, nx.number_of_edges(G))
worksheet.write(0,5, "Is the graph bipartite ?")
def test_average_path_graph():
G = nx.path_graph(4)
assert bipartite.average_clustering(G, mode="dot") == 0.5
assert bipartite.average_clustering(G, mode="max") == 0.5
assert bipartite.average_clustering(G, mode="min") == 1
pd.Series(student_counts).describe()
pd.Series(video_counts).describe()
#########################
### Build a network ###
#########################
# One group is student IDs
# The other group is video IDs
G = nx.Graph()
G.add_nodes_from(students, bipartite=0)
G.add_nodes_from(videos, bipartite=1)
G.add_edges_from(
list(zip(combined['useraccount_id'].values, combined['video_id'].values)))
cluster_all = bipartite.average_clustering(G)
density_all = bipartite.density(G)
# Get degrees for unprojected graph
student_degree = G.degree(students)
list_student_degree = [val for (node, val) in student_degree]
plt.hist(list_student_degree)
plt.xlabel('Number of Unique Videos')
plt.ylabel('Number of Students')
plt.show()
pd.Series(list_student_degree).describe()
video_degree = G.degree(videos)
list_video_degree = [val for (node, val) in video_degree]
plt.hist(list_video_degree)
plt.xlabel('Number of Unique Accessing Students')
def test_average_path_graph():
G = nx.path_graph(4)
assert bipartite.average_clustering(G, mode='dot') == 0.5
assert bipartite.average_clustering(G, mode='max') == 0.5
assert bipartite.average_clustering(G, mode='min') == 1