Temporal network alignment via GoT-WAVE

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Aparicio, D (Autor) Outra; Pedro Ribeiro (Autor) FCUP; Milenkovic, T (Autor) Outra; Silva, F (Autor) FCUP

Revista

Título: Bioinformatics

Vol. 35

Páginas: 3527-3529

ISSN: 1367-4803

Editora: Oxford University Press

Indexação

ISI Web of Knowledge - 16 Citações

Scopus - 21 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 3