@Article{cmc.2020.09807,
AUTHOR = {Senbo Chen, Wenan Tan},
TITLE = {Influence Diffusion Model in Multiplex Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {345--358},
URL = {http://www.techscience.com/cmc/v64n1/39146},
ISSN = {1546-2226},
ABSTRACT = {The problem of influence maximizing in social networks refers to obtaining a 
set of nodes of a specified size under a specific propagation model so that the aggregation 
of the node-set in the network has the greatest influence. Up to now, most of the research 
has tended to focus on monolayer network rather than on multiplex networks. But in the 
real world, most individuals usually exist in multiplex networks. Multiplex networks are 
substantially different as compared with those of a monolayer network. In this paper, we 
integrate the multi-relationship of agents in multiplex networks by considering the 
existing and relevant correlations in each layer of relationships and study the problem of 
unbalanced distribution between various relationships. Meanwhile, we measure the
distribution across the network by the similarity of the links in the different relationship
layers and establish a unified propagation model. After that, place on the established 
multiplex network propagation model, we propose a basic greedy algorithm on it. To 
reduce complexity, we combine some of the characteristics of triggering model into our 
algorithm. Then we propose a novel MNStaticGreedy algorithm which is based on the 
efficiency and scalability of the StaticGreedy algorithm. Our experiments show that the 
novel model and algorithm are effective, efficient and adaptable.},
DOI = {10.32604/cmc.2020.09807}
}