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Title: Temporal network alignment via GoT-WAVE

Abstract

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. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

Authors:
 [1];  [1];  [2];  [1];
  1. CRACS & INESC-TEC, Departamento de Ciência de Computadores, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
  2. Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
Publication Date:
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)
OSTI Identifier:
1562342
Grant/Contract Number:  
UID/EEA50014/2013
Resource Type:
Published Article
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Name: Bioinformatics Journal Volume: 35 Journal Issue: 18; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Aparício, David, Ribeiro, Pedro, Milenković, Tijana, Silva, Fernando, and Berger, ed., Bonnie. Temporal network alignment via GoT-WAVE. United Kingdom: N. p., 2019. Web. doi:10.1093/bioinformatics/btz119.
Aparício, David, Ribeiro, Pedro, Milenković, Tijana, Silva, Fernando, & Berger, ed., Bonnie. Temporal network alignment via GoT-WAVE. United Kingdom. doi:10.1093/bioinformatics/btz119.
Aparício, David, Ribeiro, Pedro, Milenković, Tijana, Silva, Fernando, and Berger, ed., Bonnie. Wed . "Temporal network alignment via GoT-WAVE". United Kingdom. doi:10.1093/bioinformatics/btz119.
@article{osti_1562342,
title = {Temporal network alignment via GoT-WAVE},
author = {Aparício, David and Ribeiro, Pedro and Milenković, Tijana and Silva, Fernando and Berger, ed., Bonnie},
abstractNote = {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. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btz119},
journal = {Bioinformatics},
number = 18,
volume = 35,
place = {United Kingdom},
year = {2019},
month = {2}
}

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This content will become publicly available on February 13, 2020
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