Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams

Eneko Osaba, David Camacho, Javier Del Ser, Akemi Galvez, Angel Panizo, Andres Iglesias

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

Finding communities of interrelated nodes is a learning task that often holds in problems that can be modeled as a graph. In any case, detecting an optimal partition in a graph is highly time-consuming and complex. For this reason, the implementation of search-based metaheuristics arises as an alternative for addressing these problems. This manuscript focuses on optimally partitioning dynamic network instances, in which the connections between vertices change dynamically along time. Specifically, the application of Novelty Search mechanism for solving the problem of finding communities in dynamic networks is studied in this paper. For this goal, this procedure has been embedded in the search process undertaken by three different bio-inspired meta-heuristic schemes: Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been properly adapted for dealing with this discrete and dynamic problem, using a reformulated expression of the modularity coefficient as its fitness function. A thorough experimentation has been conducted using a benchmark composed by 12 synthetically created instances, with the main objective of analyzing the performance of the proposed Novelty Search mechanism when facing this problem. In light of the outperforming behavior of our approach and its relevance dictated by two different statistical tests, we conclude that Novelty Search is a promising procedure for finding communities in evolving graph data.

Idioma originalInglés
Título de la publicación alojadaGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
EditorialAssociation for Computing Machinery, Inc
Páginas1329-1335
Número de páginas7
ISBN (versión digital)9781450367486
DOI
EstadoPublicada - 13 jul 2019
Evento2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, República Checa
Duración: 13 jul 201917 jul 2019

Serie de la publicación

NombreGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conferencia

Conferencia2019 Genetic and Evolutionary Computation Conference, GECCO 2019
País/TerritorioRepública Checa
CiudadPrague
Período13/07/1917/07/19

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