total_communities, cnm_communities, modularity = cnm_find_communities(
snap_graph)
print('Total communities found with CNM algorithm: ',
color='green',
log_type='info',
end='')
print('{}'.format(total_communities), color='cyan', text_format='bold')
if __name__ == '__main__':
"""
Parse arguments and follow through to mission control
"""
# Initial message
file_operations.initial_message(os.path.basename(__file__),
'(SNAP) Clauset-Newman-Moore algorithm')
# Create parser
parser = argparse.ArgumentParser(
prog='na_cnm.py',
usage='python %(prog)s <input_file> <options>',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
SNAP Clauset-Newman-Moore Algorithm
'''),
epilog='',
add_help=True)
parser.add_argument(
'-i',
'--input-file',
:param input_file: Input file path
:param delimiter: Optional separator for he column of the input file
:param weighted: Simple yes/no if the input file is weighted or not
:rtype: <>
"""
print('Initializing.....', color='green', log_type='info')
numeric_mapper(input_file, delimiter, weighted)
# Standard boilerplate for running this source code file as a standalone segment
if __name__ == '__main__':
"""
Parse arguments and follow through to mission control
"""
# Print initial message
file_operations.initial_message(os.path.basename(__file__))
# Create parser
parser = argparse.ArgumentParser(add_help=True)
parser.add_argument(
'-i',
'--input-file',
action='store',
dest='input_file',
required=True,
help=
'Input file absolute path. E.g. /home/user/data/input/file_name.txt/.csv/.dat etc.'
)
parser.add_argument(
'-d',
:param delimiter: Column separator
:param weighted: Is the file has a weight column? (yes/no)
:param output: Boolean, yes/no if the output file will be created or not
:return: <>
"""
print('Initializing.....', log_type='info')
run_louvain(input_file, delimiter, weighted, output)
# Standard boilerplate for running this source code file as a standalone segment
if __name__ == '__main__':
"""
Parse arguments and follow through to mission control
"""
# Initial message
file_operations.initial_message(os.path.basename(__file__),
'Louvain method')
# Create parser
parser = argparse.ArgumentParser(
prog='na_louvain.py',
usage='python %(prog)s <input_file> <options>',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
This program uses Louvain method algorithm to detect communities in large-scale
networks. For more please visit: https://github.com/taynaud/python-louvain
'''),
epilog='',
add_help=True)
parser.add_argument(
'-i',
:return: <>
"""
print('Initializing.....', color='green', log_type='info')
# Clip text according to input
clip_text(input_file, delimiter, start_date, interval)
pass
# Standard boilerplate for running this source code file as a standalone segment
if __name__ == '__main__':
"""
Parse arguments and follow through to mission control
"""
# Print initial message
message = 'This script uses linux timestamps. Input file format (source target weight timestamp)'
file_operations.initial_message(os.path.basename(__file__),
custom_message=message)
# Create parser
parser = argparse.ArgumentParser(add_help=True)
parser.add_argument(
'-i',
'--input-file',
action='store',
dest='input_file',
required=True,
help=
'Input file absolute path. E.g. /home/user/data/input/file_name.txt')
parser.add_argument(
'-d',
'--delimiter',
:param weighted: are the edges weighted?
:param trials: number of trials/run to find out community
:param output: whether output file will be created or not (boolean - yes/no)
:return: NULL
"""
print('Initializing.....', log_type='info')
run_infomap(input_file, delimiter, weighted, trials, output)
# Standard boilerplate for running this source code file as a standalone segment
if __name__ == '__main__':
"""
Parse arguments and follow through to mission control
"""
# Initial message
file_operations.initial_message(os.path.basename(__file__), 'Infomap')
# Create parser
parser = argparse.ArgumentParser(prog='na_infomap.py',
usage='python %(prog)s <input_file> <options>',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
This program uses Infomap algorithm to detect communities in large-scale networks.
For more please visit: http://www.mapequation.org
To find out available <options> for this program please visit:
http://www.mapequation.org/code.html#Options
'''),
epilog='',
add_help=True)
parser.add_argument('-i', '--input-file', action='store', dest='input', required=True,