Temporal network alignment via GoT-WAVE

Journal Article · 12 February 2019 · Bioinformatics

DOI: https://doi.org/10.1093/bioinformatics/btz119 · OSTI ID:1562342

Aparício, David; Ribeiro, Pedro; Milenković, Tijana; Silva, Fernando; Berger, ed., Bonnie

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.

• Sponsoring Organization: USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain
• OSTI ID: 1562342
• Journal Information: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 18 Vol. 35; ISSN 1367-4803
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English