  The information flows among the people while they communicate through social
media websites. Due to the dependency on digital media, a person shares
important information or regular updates with friends and family. The set of
persons on social media forms a social network. Influence Maximization (IM) is
a known problem in social networks. In social networks, information flows from
one person to another using an underlying diffusion model. There are two
fundamental diffusion models: the Independent Cascade Model (ICM) and the
Linear Threshold Model (LTM). In this paper, we study a variant of the IM
problem called Minimum Influential Seeds (MINFS) problem proposed by Qiang et
al.[16]. It generalizes the classical IM problem with LTM as the diffusion
model. Compared to IM, this variant has additional parameters: the influence
threshold for each node and the propagation range. The propagation range is a
positive integer that specifies how far the information can propagate from a
node. A node on the network is not immediately influenced until it receives the
same information from enough number of neighbors (influence threshold).
Similarly, any node does not forward information until it receives the same
information from a sufficient number of neighbors (activation threshold). Once
a node becomes activated, it tries to activate or influence its neighbors. The
MINFS problem aims to select the minimum number of initial spreader nodes such
that all nodes of the graph are influenced. In this paper, we extend the study
of the MINFS problem. We propose heuristics that construct seed sets based on
the average degree of non-activated nodes, closest first, and backbone-based
heaviest path.
