def compute_adjency(path, name):
adjency = defaultdict(list)
with open(path + name) as f:
sections = list(per_section(f))
for elt in sections[0]:
adjency[int(elt.split(',')[0])].append(int(elt.split(',')[1]))
return adjency
def graph_indicator(path, name):
data_dict = defaultdict(list)
with open(path + name) as f:
sections = list(per_section(f))
k = 1
for elt in sections[0]:
data_dict[int(elt)].append(k)
k = k + 1
return data_dict
def graph_label_list(path, name):
graphs = []
with open(path + name) as f:
sections = list(per_section(f))
k = 1
for elt in sections[0]:
graphs.append((k, int(elt)))
k = k + 1
return graphs
def node_labels_dic(path, name):
node_dic = dict()
with open(path + name) as f:
sections = list(per_section(f))
k = 1
for elt in sections[0]:
node_dic[k] = int(elt)
k = k + 1
return node_dic
def node_attr_dic(path, name):
node_dic = dict()
with open(path + name) as f:
sections = list(per_section(f))
k = 1
for elt in sections[0]:
node_dic[k] = [float(x) for x in elt.split(',')]
k = k + 1
return node_dic
def node_attr_dic(path, name):
node_dic = dict()
with open(path + name) as f:
sections = list(per_section(f))
k = 1
for elt in sections[0]:
node_dic[k] = [round(float(x), 4) for x in elt.split(',')]
if np.isnan(node_dic[k]).any(): # then there are None values
node_dic[k] = [
0.00 if math.isnan(x) else x for x in node_dic[k]
] # remove NaNs
else:
node_dic[k] = [x for x in node_dic[k]
] # x/max(node_dic[k])normalize
node_dic[k] = [x for x in node_dic[k][:]]
k = k + 1
return node_dic
