Print Email Facebook Twitter Markov-based solution for information diffusion on adaptive social networks Title Markov-based solution for information diffusion on adaptive social networks Author Liu, Chuang (Hangzhou Normal University) Zhou, Nan (Hangzhou Normal University) Zhan, X. (TU Delft Multimedia Computing) Sun, Gui-Quan (Shanxi University; North University of China) Zhang, Zi-Ke (Shanxi University; Hangzhou Normal University) Date 2020 Abstract There is currently growing interest in modeling the information diffusion on social networks across multi-disciplines, including the prediction of the news popularity, the detection of the rumors and the influence of the epidemiological studies. Following the framework of the epidemic spreading, the information spreading models assume that information can be transmitted from the known individuals (infected) to the un-known individuals (susceptible) through the network interactions. During this process, individuals also always change their interactions which in turn will greatly influence the information spreading. In this work, we propose a mechanism considering the co-evolution between information states and network topology simultaneously, in which the information diffusion was executed as an SIS process and network topology evolved based on the adaptive assumption. The theoretical analyses based on the Markov approach were very consistent with simulation. Both simulation results and theoretical analyses indicated that the adaptive process, in which informed individuals would rewire the links between the informed neighbors to a random non-neighbor node, can enhance information diffusion (leading to much broader spreading). In addition, we obtained that two threshold values exist for the information diffusion on adaptive networks, i.e., if the information propagation probability is less than the first threshold, information cannot diffuse and dies out immediately; if the propagation probability is between the first and second threshold, information will spread to a finite range and die out gradually; and if the propagation probability is larger than the second threshold, information will diffuse to a certain size of population in the network. These results may shed some light on understanding the co-evolution between information diffusion and network topology. Subject Adaptive social networksCo-evolutionInformation spreading To reference this document use: http://resolver.tudelft.nl/uuid:6e97a477-3a0c-43bb-8c75-9e628c071962 DOI https://doi.org/10.1016/j.amc.2020.125286 Embargo date 2020-11-01 ISSN 0096-3003 Source Applied Mathematics and Computation, 380, 1-10 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2020 Chuang Liu, Nan Zhou, X. Zhan, Gui-Quan Sun, Zi-Ke Zhang Files PDF 1_s2.0_S0096300320302551_main.pdf 1.8 MB Close viewer /islandora/object/uuid:6e97a477-3a0c-43bb-8c75-9e628c071962/datastream/OBJ/view