TY  - JOUR
ID  - SisLab4073
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4073/
IS  - 8 (183
A1  - Hoang, Xuan Huan
A1  - Pham, Van Canh
A1  - Ha, K.T. Dung
A1  - Vu, C Quang
A1  - Nguyen, Su Anh
Y1  - 2020///
N2  - The 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 ? ? with e > 0, ? ? (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.
PB  - MDPI
JF  - MDPI algorithms
VL  - 13
SN  - 1999-4893
TI  - Influence Maximization with Priority in Online
Social Networks
SP  - 1
AV  - public
EP  - 23
ER  -