Aparício D, Ribeiro P, Milenković T, Silva F. Temporal network alignment via GoT-WAVE.
Bioinformatics 2020;
35:3527-3529. [PMID:
30759185 DOI:
10.1093/bioinformatics/btz119]
[Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 01/20/2019] [Accepted: 02/12/2019] [Indexed: 11/14/2022] Open
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.
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