Here you can find the code associated with the paper "An Efficient Procedure for Mining Egocentric Temporal Motifs". paper
You can find a tutorial here
Download the repository and import files as follow:
import construction as cs
from ETN import *
from ETMM import *then, given a temporal graph represented in an edge list (like those in Dataset/) and a temporal gap, you can build an ordered sequence of static snapshots with:
# Parameters
gap = 299 # temporal gap
file_name = "InVS13" # name of the file
data = cs.load_data("Datasets/"+file_name+".dat")
graphs = cs.build_graphs(data,gap=gap,with_labels=False)Since the array of static graphs is computed you can count ETN (given k) simply by:
S = count_ETN(graphs,k,meta=meta_data)
S = {k: v for k, v in sorted(S.items(), key=lambda item: item[1], reverse=1)}
store_etns(S,file_name,gap,k,label=label) # store the ETN counts[1] Longa, A. et al (2021). An Efficient Procedure for Mining Egocentric Temporal Motifs.