  A social network (SN) is a social structure consisting of a group
representing the interaction between them. SNs have recently been widely used
and, subsequently, have become suitable and popular platforms for product
promotion and information diffusion. People in an SN directly influence each
other's interests and behavior. One of the most important problems in SNs is to
find people who can have the maximum influence on other nodes in the network in
a cascade manner if they are chosen as the seed nodes of a network diffusion
scenario. Influential diffusers are people who, if they are chosen as the seed
set in a publishing issue in the network, that network will have the most
people who have learned about that diffused entity. This is a well-known
problem in literature known as influence maximization (IM) problem. Although it
has been proven that this is an NP-complete problem and does not have a
solution in polynomial time, it has been argued that it has the properties of
sub modular functions and, therefore, can be solved using a greedy algorithm.
Most of the methods proposed to improve this complexity are based on the
assumption that the entire graph is visible. However, this assumption does not
hold for many real-world graphs. This study is conducted to extend current
maximization methods with link prediction techniques to pseudo-visibility
graphs. To this end, a graph generation method called the exponential random
graph model (ERGM) is used for link prediction. The proposed method is tested
using the data from the Snap dataset of Stanford University. According to the
experimental tests, the proposed method is efficient on real-world graphs.
