VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-09-15T10:09:08ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2020-10-13T08:18:56Z2020-10-13T08:20:11Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4073This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/40732020-10-13T08:18:56ZInfluence Maximization with Priority in Online
Social NetworksThe Influence Maximization (IM) problem, which finds a set of k nodes (called seedset)
in a social network to initiate the influence spread so that the number of influenced nodes after
propagation process is maximized, is an important problem in information propagation and social
network analysis. However, previous studies ignored the constraint of priority that led to inefficient
seed collections. In some real situations, companies or organizations often prioritize influencing
potential users during their influence diffusion campaigns. With a new approach to these existing
works, we propose a new problem called Influence Maximization with Priority (IMP) which finds out
a set seed of k nodes in a social network to be able to influence the largest number of nodes subject
to the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in
this paper. We show that the problem is NP-hard under well-known IC model. To find the solution,
we propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS)
with provable theoretical guarantees. IG provides a �
1 − (1 − 1
k
)
t
�
-approximation solution with t
is an outcome of algorithm and t ≥ 1. The worst-case approximation ratio is obtained when t = 1
and it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based
on sampling method that provides a �
1 − (1 − 1
k
)
t − e
�
-approximation solution with probability
at least 1 − δ with e > 0, δ ∈ (0, 1) as input parameters of the problem. We conduct extensive
experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms
in IM problem. The results indicate that our algorithm provides better solutions interns of influence
on the priority sets when approximately give twice to ten times higher than threshold T while running
time, memory usage and the influence spread also give considerable results compared to the others.Xuan Huan Hoanghuanhx@vnu.edu.vnVan Canh PhamK.T. Dung HaC Quang VuSu Anh Nguyen2016-12-30T08:26:32Z2016-12-30T08:26:32Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2373This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23732016-12-30T08:26:32ZA new viral marketing strategy with the competition in the large-scale Online Social NetworksThe problem of Influence Maximization (IM) on social networks proposed firstly by Kempe et al. (2003) has been researched and developed with many cases. However, the IM in limited time while unwanted users are restricted is still a new potential subject. In this paper, we conducted research the problem on the model of information diffusion name Locally Bounded Diffusion and tested some useful heuristic algorithms. The results of the experiment on some real datasets of social networks show that the algorithm meta-heuristic generated better output than the others.Van Canh PhamKim Dung HaQuang Dung NgoQuang Cao VuXuan Huan Hoang2016-12-29T12:13:36Z2016-12-29T12:13:36Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2372This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23722016-12-29T12:13:36ZTime-Critical Viral Marketing Strategy with the Competition on Online Social NetworksVan Canh PhamTra My ThaiKim Dung HaQuang Dung NgoXuan Huan Hoanghuanhx@vnu.edu.vn2016-06-04T08:43:23Z2016-06-04T08:43:23Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1718This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/17182016-06-04T08:43:23ZPreventing and detecting infiltration on Online Social NetworksVan Canh PhamXuan Huan Hoanghuanhx@vnu.edu.vnMinh Manh Vu