Temporal network alignment via GoT-WAVE David Aparício, Pedro Ribeiro, Tijana Milenković and Fernando Silva 2019

Abstract

Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results: On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.

Keywords

Network Alignment; Graphlets; Temporal Networks

Digital Object Identifier (DOI)

doi 10.1093/bioinformatics/btz119

Publication in PDF format

pdf Download PDF

Software

software GoT-WAVE

Journal/Conference/Book

Bioinformatics

Reference (text)

David Aparício, Pedro Ribeiro, Tijana Milenković and Fernando Silva. Temporal network alignment via GoT-WAVE. In Bioinformatics, Vol. 35(18), pp. 3527-3529, Oxford University Press, September, 2019.

Bibtex

@article{ribeiro-BIOINFORMATICS2019,
  author = {David Aparício and  Pedro Ribeiro and  Tijana Milenković and Fernando Silva},
  title = {Temporal network alignment via GoT-WAVE},
  doi = {10.1093/bioinformatics/btz119},
  journal = {Bioinformatics},
  volume = {35},
  issue = {18},
  pages = {3527-3529},
  publisher = {Oxford University Press},
  month = {September},
  year = {2019}
}