{"ID":2849930,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22501","arxiv_id":"2510.22501","title":"A Novel Discrete-time Model of Information Diffusion on Social Networks Considering Users Behavior","abstract":"In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventually choose not to share it at all. Based on the mean-field approximation method, we derive the dynamical equations of the model and investigate its convergence and stability conditions. Under these conditions, we further propose an approximation algorithm for the edge-deletion problem, aiming to minimize the influence of information diffusion by identifying approximate solutions.","short_abstract":"In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but...","url_abs":"https://arxiv.org/abs/2510.22501","url_pdf":"https://arxiv.org/pdf/2510.22501v2","authors":"[\"Tran Van Khanh\",\"Do Xuan Cho\",\"Hoang Phi Dung\"]","published":"2025-10-26T02:43:35Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.IT\",\"math.OC\"]","methods":"[\"Diffusion Model\"]","has_code":false}