Abstract
The spread of malicious information such as fake news or rumors in Online Social Networks (OSNs) has aroused keen interest for researchers to develop countermeasures for reducing this information side effects. Thus, this work proposes to investigate the rumor influence minimization problem in dynamic multilayer networks. This work introduces a new representation of an OSN. The new representation is based on dynamic multilayer networks with heterogeneous propagation models for each layer. Second, we present a hybrid approach between the Nodes or Links Blocking Strategy (BNLS) and the Truth Campaign Strategy (TCS). The main goal is to determine an optimal set of nodes to be selected for BNLS and TCS that reduce the most the impact of a rumor. We show that the proposed method could capture: (1) the new representation of an OSN; (2) the dynamic evolution of a network structure, and (3) the dynamic aspect of the propagation of a rumor. Systematically, experiments have been conducted to evaluate the performance of the proposed approach on synthetic/real networks and single/multiple layer networks. The results showed that the proposed approach could outperform the most recent methods in the literature.
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Hosni, A.I.E., Hafiani, K.A., Chenoui, A., Beghdad Bey, K. (2022). Hybrid Approach for Rumor Influence Minimization in Dynamic Multilayer Online Social Networks. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_24
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