<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Influence Maximization with Priority in Online\r\nSocial Networks"^^ . "The Influence Maximization (IM) problem, which finds a set of k nodes (called seedset)\r\nin a social network to initiate the influence spread so that the number of influenced nodes after\r\npropagation process is maximized, is an important problem in information propagation and social\r\nnetwork analysis. However, previous studies ignored the constraint of priority that led to inefficient\r\nseed collections. In some real situations, companies or organizations often prioritize influencing\r\npotential users during their influence diffusion campaigns. With a new approach to these existing\r\nworks, we propose a new problem called Influence Maximization with Priority (IMP) which finds out\r\na set seed of k nodes in a social network to be able to influence the largest number of nodes subject\r\nto the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in\r\nthis paper. We show that the problem is NP-hard under well-known IC model. To find the solution,\r\nwe propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS)\r\nwith provable theoretical guarantees. IG provides a �\r\n1 − (1 − 1\r\nk\r\n)\r\nt\r\n�\r\n-approximation solution with t\r\nis an outcome of algorithm and t ≥ 1. The worst-case approximation ratio is obtained when t = 1\r\nand it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based\r\non sampling method that provides a �\r\n1 − (1 − 1\r\nk\r\n)\r\nt − e\r\n�\r\n-approximation solution with probability\r\nat least 1 − δ with e > 0, δ ∈ (0, 1) as input parameters of the problem. We conduct extensive\r\nexperiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms\r\nin IM problem. The results indicate that our algorithm provides better solutions interns of influence\r\non the priority sets when approximately give twice to ten times higher than threshold T while running\r\ntime, memory usage and the influence spread also give considerable results compared to the others."^^ . "2020" . . "13" . "8 (183" . . "MDPI"^^ . . . "MDPI algorithms"^^ . . . "19994893" . . . . . . . . . . . . . . . . . . . "Su Anh"^^ . "Nguyen"^^ . "Su Anh Nguyen"^^ . . "C Quang"^^ . "Vu"^^ . "C Quang Vu"^^ . . "Xuan Huan"^^ . "Hoang"^^ . "Xuan Huan Hoang"^^ . . "Van Canh"^^ . "Pham"^^ . "Van Canh Pham"^^ . . "K.T. Dung"^^ . "Ha"^^ . "K.T. Dung Ha"^^ . . "Canh V. Pham"^^ . . . "Dung K. T. Ha"^^ . . . "Quang C. Vu"^^ . . . "Anh N. Su"^^ . . . . . . . "Influence Maximization with Priority in Online\r\nSocial Networks (PDF)"^^ . . . "B54.pdf"^^ . . . "Influence Maximization with Priority in Online\r\nSocial Networks (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Influence Maximization with Priority in Online\r\nSocial Networks (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Influence Maximization with Priority in Online\r\nSocial Networks (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Influence Maximization with Priority in Online\r\nSocial Networks (Other)"^^ . . . . . . "small.jpg"^^ . . . "Influence Maximization with Priority in Online\r\nSocial Networks (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #4073 \n\nInfluence Maximization with Priority in Online \nSocial Networks\n\n" . "text/html" . . . "Information Technology (IT)"@en . . . "Scopus-indexed journals"@en . .