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