Temporal network alignment via GoT-WAVE

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Aparicio, D (Author) Other; Pedro Ribeiro (Author) FCUP; Milenkovic, T (Author) Other; Silva, F (Author) FCUP

Journal

Title: Bioinformatics

Vol. 35

Pages: 3527-3529

ISSN: 1367-4803

Publisher: Oxford University Press

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 21 Citations

Other information

Authenticus ID: P-00Q-7SX

DOI: 10.1093/bioinformatics/btz119

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3