def run_motif_significance(graph,
directed=True,
data_loc="../data/",
motif_size=3,
n_shuffles=16,
s_model='uncorrelated'):
"""Run z-score computation for all `motif_size` subgraph
on the given `graph`. By default, graph is loaded as a directed graph_tool
instance.
Parameters
==========
graph: name of the graph file."""
f_name = data_loc + graph + ".edges"
g = load_graph_from_csv(f_name,
directed,
csv_options={
'quotechar': '"',
'delimiter': ' '
})
m, z = motif_significance(g, motif_size, n_shuffles, shuffle_model=s_model)
motif_annotation = str(motif_size) + 'm' if directed else str(
motif_size) + 'um'
output_name = "{}{}_{}.{}".format(data_loc, graph, motif_annotation,
"motifslog")
return write_motifs_results(output_name, m, z, n_shuffles, s_model)
def main():
with open("/home/usr8/15M54097/motifwalk/data/youtube_gt.data", "rb") as f:
ytgt = pickle.load(f)
yt_motif4_results = motif_significance(ytgt,
k=4,
n_shuffles=10,
full_output=True)
with open("./youtube_motif_results.pkl", "wb") as f:
pickle.dump(yt_motif4_results, f)
return 0
def motifs(g: Graph, k: int = 3):
return clustering.motif_significance(g, k)