def line(G,
scale=1,
iter_count=50,
sfdp_path=getcwd() + '/SFDP/sfdp_osx',
representation_size=64,
window_size=1,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=0,
sample=0.001):
return graph_coarsening.skipgram_coarsening_disconnected(
G,
scale=scale,
iter_count=iter_count,
sfdp_path=sfdp_path,
representation_size=representation_size,
window_size=window_size,
lr_scheme=lr_scheme,
alpha=alpha,
min_alpha=min_alpha,
sg=sg,
hs=hs,
sample=sample)
def deepwalk(G,
scale=-1,
iter_count=1,
sfdp_path=getcwd() + '/SFDP/sfdp_osx',
num_walks=40,
walk_length=10,
representation_size=128,
window_size=10,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=1,
coarsening_scheme=2,
sample=0.1):
return graph_coarsening.skipgram_coarsening_disconnected(
G,
scale=scale,
iter_count=iter_count,
sfdp_path=sfdp_path,
num_paths=num_walks,
path_length=walk_length,
representation_size=representation_size,
window_size=window_size,
lr_scheme=lr_scheme,
alpha=alpha,
min_alpha=min_alpha,
sg=sg,
hs=hs,
coarsening_scheme=coarsening_scheme,
sample=sample)
def dw(G,
scale=-1,
iter_count=1,
sfdp_path=getcwd() + '/SFDP/sfdp_osx',
num_walks=40,
walk_length=10,
representation_size=128,
window_size=10,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=1,
coarsening_scheme=2,
sample=0.1,
outfile='emb.harp.wv',
workers=1,
negative=5,
min_count=0):
embeddings = graph_coarsening.skipgram_coarsening_disconnected(
G,
scale=scale,
iter_count=iter_count,
sfdp_path=sfdp_path,
num_paths=num_walks,
path_length=walk_length,
min_count=min_count,
representation_size=representation_size,
window_size=window_size,
negative=negative,
lr_scheme=lr_scheme,
alpha=alpha,
min_alpha=min_alpha,
sg=sg,
hs=hs,
coarsening_scheme=coarsening_scheme,
sample=sample,
workers=workers)
to_w2v(embeddings, outfile)
def test_harp(input_file, output_file, sfdp):
G = magicgraph.load_edgelist(input_file, undirected=True)
# G = magicgraph.load_matfile(input_file, variable_name='network', undirected=True)
G = graph_coarsening.DoubleWeightedDiGraph(G)
print('Number of nodes: {}'.format(G.number_of_nodes()))
print('Number of edges: {}'.format(G.number_of_edges()))
embeddings = graph_coarsening.skipgram_coarsening_disconnected(
G,
scale=-1,
iter_count=1,
sfdp_path=sfdp,
num_paths=10,
path_length=80,
representation_size=128,
window_size=10,
lr_scheme='default',
alpha=0.025,
min_alpha=0.001,
sg=1,
hs=0,
coarsening_scheme=2,
sample=0.1)
np.save(output_file, 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)