def Coarsening(filename):
G = magicgraph.load_edgelist(filename, undirected=True)
G = graph_coarsening.DoubleWeightedDiGraph(G)
print('Orginal Graph')
print('number of nodes', G.number_of_nodes())
print('number of edges', G.number_of_edges())
sfdp_path = '/home/diana/Benchmark/HARP/bin/sfdp_linux'
Gs, mps = graph_coarsening.external_ec_coarsening(G, sfdp_path)
return Gs, mps
def main():
parser = ArgumentParser('harp',
formatter_class=ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('--format',
default='mat',
help='File format of input file')
parser.add_argument('--input',
nargs='?',
required=True,
help='Input graph file')
parser.add_argument(
'--sfdp-path',
default='./bin/sfdp_osx',
help=
'Path to the SFDP binary file which produces graph coarsening results.'
)
parser.add_argument(
'--model',
default='deepwalk',
help='Embedding model to use. Could be deepwalk, line or node2vec.')
parser.add_argument(
'--matfile-variable-name',
default='network',
help='Variable name of adjacency matrix inside a .mat file')
parser.add_argument('--number-walks',
default=40,
type=int,
help='Number of random walks to start at each node')
parser.add_argument('--output',
required=True,
help='Output representation file')
parser.add_argument(
'--representation-size',
default=128,
type=int,
help='Number of latent dimensions to learn for each node.')
parser.add_argument('--walk-length',
default=10,
type=int,
help='Length of the random walk started at each node.')
parser.add_argument('--window-size',
default=10,
type=int,
help='Window size of the Skip-gram model.')
parser.add_argument(
'--workers',
default=1,
type=int,
help=
'Number of parallel processes, -1 to consume all available logical CPUs (harware threads).'
)
args = parser.parse_args()
# Process args
if args.format == 'mat':
G = magicgraph.load_matfile(args.input,
variable_name=args.matfile_variable_name,
undirected=True)
elif args.format == 'adjlist':
G = magicgraph.load_adjacencylist(args.input, undirected=True)
elif args.format == 'edgelist':
G = magicgraph.load_edgelist(args.input, undirected=True)
else:
raise ValueError(
"Unknown file format: '{}'. Valid formats: 'mat', 'adjlist', and 'edgelist'."
.format(args.format))
if args.workers < 0 or args.workers > cpu_count():
if args.workers == -1:
args.workers = cpu_count()
else:
raise ValueError("Invalid number of workers: {} / {} CPUs".format(
args.workers, cpu_count()))
G = graph_coarsening.DoubleWeightedDiGraph(G) # makeContiguous
print('Number of nodes: {}'.format(G.number_of_nodes()))
print('Number of edges: {}'.format(G.number_of_edges()))
print('Underlying network embedding model: {}'.format(args.model))
outpbase = os.path.splitext(args.output)[0]
if args.model == 'deepwalk':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(
G,
workers=args.workers,
output=
outpbase, # Retain contigious raw internal ids for the embeddings
scale=-1,
iter_count=1,
sfdp_path=args.sfdp_path,
num_paths=args.number_walks,
path_length=args.walk_length,
representation_size=args.representation_size,
window_size=args.window_size,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=1,
coarsening_scheme=2,
sample=0.1)
elif args.model == 'node2vec':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(
G,
workers=args.workers,
output=outpbase,
scale=-1,
iter_count=1,
sfdp_path=args.sfdp_path,
num_paths=args.number_walks,
path_length=args.walk_length,
representation_size=args.representation_size,
window_size=args.window_size,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=0,
coarsening_scheme=2,
sample=0.1)
elif args.model == 'line':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(
G,
workers=args.workers,
output=outpbase,
scale=1,
iter_count=50,
sfdp_path=args.sfdp_path,
representation_size=args.representation_size,
window_size=1,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=0,
sample=0.001)
if args.output.lower().endswith('.mat'):
savemat(args.output, mdict={'embs': embeddings})
else:
np.save(args.output, embeddings)
def main():
parser = ArgumentParser('harp',
formatter_class=ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('--format', default='mat',
help='File format of input file')
parser.add_argument('--input', nargs='?', required=True,
help='Input graph file')
parser.add_argument('--sfdp-path', default='./bin/sfdp_osx',
help='Path to the SFDP binary file which produces graph coarsening results.')
parser.add_argument('--model', default='deepwalk',
help='Embedding model to use. Could be deepwalk, line or node2vec.')
parser.add_argument('--matfile-variable-name', default='network',
help='Variable name of adjacency matrix inside a .mat file')
parser.add_argument('--number-walks', default=40, type=int,
help='Number of random walks to start at each node')
parser.add_argument('--output', required=True,
help='Output representation file')
parser.add_argument('--representation-size', default=128, type=int,
help='Number of latent dimensions to learn for each node.')
parser.add_argument('--walk-length', default=10, type=int,
help='Length of the random walk started at each node.')
parser.add_argument('--window-size', default=10, type=int,
help='Window size of the Skip-gram model.')
parser.add_argument('--workers', default=1, type=int,
help='Number of parallel processes.')
args = parser.parse_args()
# Process args
if args.format == 'mat':
G = magicgraph.load_matfile(args.input, variable_name=args.matfile_variable_name, undirected=True)
elif args.format == 'adjlist':
G = magicgraph.load_adjacencylist(args.input, undirected=True)
elif args.format == 'edgelist':
G = magicgraph.load_edgelist(args.input, undirected=True)
else:
raise Exception("Unknown file format: '%s'. Valid formats: 'mat', 'adjlist', and 'edgelist'."
% args.format)
G = graph_coarsening.DoubleWeightedDiGraph(G)
# print ('Number of nodes: {}'.format(G.number_of_nodes()))
# print ('Number of edges: {}'.format(G.number_of_edges()))
# print ('Underlying network embedding model: {}'.format(args.model))
if args.model == 'deepwalk':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(G,scale=-1,iter_count=1,
sfdp_path=args.sfdp_path,
num_paths=args.number_walks,path_length=args.walk_length,
representation_size=args.representation_size,window_size=args.window_size,
lr_scheme='default',alpha=0.025,min_alpha=0.001,sg=1,hs=1,coarsening_scheme=2, sample=0.1)
elif args.model == 'node2vec':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(G,scale=-1,iter_count=1,
sfdp_path=args.sfdp_path,
num_paths=args.number_walks,path_length=args.walk_length,
representation_size=args.representation_size,window_size=args.window_size,
lr_scheme='default',alpha=0.025,min_alpha=0.001,sg=1,hs=0,coarsening_scheme=2, sample=0.1)
elif args.model == 'line':
embeddings = graph_coarsening.skipgram_coarsening_disconnected(G,scale=1, iter_count=50,
sfdp_path=args.sfdp_path,
representation_size=64,window_size=1,
lr_scheme='default',alpha=0.025,min_alpha=0.001,sg=1,hs=0,sample=0.001)
np.save(args.output, embeddings)