  Social networks have enabled user-specific advertisements and recommendations
on their platforms, which puts a significant focus on Influence Maximisation
(IM) for target advertising and related tasks. The aim is to identify nodes in
the network which can maximize the spread of information through a diffusion
cascade. We propose a community structures-based approach that employs K-Shell
algorithm with community structures to generate a score for the connections
between seed nodes and communities. Further, our approach employs entropy
within communities to ensure the proper spread of information within the
communities. We validate our approach on four publicly available networks and
show its superiority to four state-of-the-art approaches while still being
relatively efficient.
