AntonioLonga / Egocentric-Temporal-Motifs-Miner-ETMM

Egocentric Temporal Motifs Miner

Egocentric Temporal Motifs Miner (ETMM)

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

Installation

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

References

[1] Longa, A. et al (2021). An Efficient Procedure for Mining Egocentric Temporal Motifs.

License

MIT