AntNetAlign: A software package for network alignment
In this paper we introduce AntNetAlign, an open-source tool (written in C++) that implements an Ant Colony Optimization (ACO) for solving the Network Alignment (NA) problem, a well-known hard optimization problem with important applications in different areas, such as biology or social networks. Spe...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/386762 |
| Acceso en línea: | https://hdl.handle.net/2117/386762 https://dx.doi.org/10.1016/j.simpa.2023.100476 |
| Access Level: | acceso abierto |
| Palabra clave: | Combinatorial optimization Ant algorithms Network alignment Open source software Optimization Ant colony optimization Optimització combinatòria Algorismes de les colònies de formigues Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | In this paper we introduce AntNetAlign, an open-source tool (written in C++) that implements an Ant Colony Optimization (ACO) for solving the Network Alignment (NA) problem, a well-known hard optimization problem with important applications in different areas, such as biology or social networks. Specifically, given two input networks, the tool finds an alignment between them (i.e., a mapping between the respective nodes) that optimizes one out of the three main topological measures. Additionally, it can make use of user-defined pairwise similarities between nodes during its construction phase, allowing for the use of more application-dependent information in order to increase its performance. Results show that AntNetAlign outperforms other state-of-the-art algorithms in two out of the three aforementioned topological scores within a reasonable amount of time, and is able to achieve competitive results in the context of larger instances (Rodríguez Corominas et al., 2023). Furthermore, a new version of the algorithm, which makes use of Negative Learning, was able to further improve these results, specially in the EC score (Corominas et al., 2022). |
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