Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks

• Autorzy: Chacón Sartori Camilo , Blum Christian

Identyfikatory

DOI

10.15439/2022F78

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

In this paper we solve a variant of the multi-hop influence maximization problem in social networks by means of a hybrid algorithm that combines a biased random key genetic algorithm with a graph neural network. Hereby, the predictions of the graph neural network are used with the biased random key genetic algorithm for a more accurate translation of individuals into valid solutions to the tackled problem. The obtained results show that the hybrid algorithm is able to outperform both the biased random key genetic algorithm and the graph neural network when used as standalone techniques. In other words, we were able to show that an integration of both techniques leads to a better algorithm.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 363--371
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Chacón Sartori Camilo

autor

Blum Christian