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