TY - GEN
T1 - Least cost rumor influence minimization in multiplex social networks
AU - Hosni, Adil Imad Eddine
AU - Li, Kan
AU - Ding, Cangfeng
AU - Ahmed, Sadique
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - This paper deals with the issue of rumors propagation in online social networks (OSNs) that are connected through overlapping users, named multiplex OSNs. We consider a strategy to initiate an anti-rumor campaign to raise the awareness of individuals and prevent the adoption of the rumor for further limiting its influence. Therefore, we introduce the Least Cost Anti-rumor Campaign (LCAC) problem to minimize the influence of the rumor. The proposed problem defines the minimum number of users to initiate this campaign, which reaches a large number of overlapping users to increase the awareness of individuals across networks. Due to the NP-hardness of LCAC problem, we prove that its objective function is submodular and monotone. Then, we introduce a greedy algorithm for LCAC problem that guarantees an approximation within (1-1/e) of the optimal solution. Finally, experiments on real-world and synthetics multiplex networks are conducted to investigate the effect of the number of the overlapping users as well as the networks structure topology. The results provide evidence about the efficacy of the proposed algorithm to limit the spread of a rumor.
AB - This paper deals with the issue of rumors propagation in online social networks (OSNs) that are connected through overlapping users, named multiplex OSNs. We consider a strategy to initiate an anti-rumor campaign to raise the awareness of individuals and prevent the adoption of the rumor for further limiting its influence. Therefore, we introduce the Least Cost Anti-rumor Campaign (LCAC) problem to minimize the influence of the rumor. The proposed problem defines the minimum number of users to initiate this campaign, which reaches a large number of overlapping users to increase the awareness of individuals across networks. Due to the NP-hardness of LCAC problem, we prove that its objective function is submodular and monotone. Then, we introduce a greedy algorithm for LCAC problem that guarantees an approximation within (1-1/e) of the optimal solution. Finally, experiments on real-world and synthetics multiplex networks are conducted to investigate the effect of the number of the overlapping users as well as the networks structure topology. The results provide evidence about the efficacy of the proposed algorithm to limit the spread of a rumor.
KW - Multiplex online social networks
KW - Optimization
KW - Rumor influence minimization
KW - Rumor propagation
UR - http://www.scopus.com/inward/record.url?scp=85059013498&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04224-0_9
DO - 10.1007/978-3-030-04224-0_9
M3 - Conference contribution
AN - SCOPUS:85059013498
SN - 9783030042233
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 105
BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
A2 - Cheng, Long
A2 - Leung, Andrew Chi Sing
A2 - Ozawa, Seiichi
PB - Springer Verlag
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
Y2 - 13 December 2018 through 16 December 2018
ER -