  In recent years, social networking platforms have gained significant
popularity among the masses like connecting with people and propagating ones
thoughts and opinions. This has opened the door to user-specific advertisements
and recommendations on these platforms, bringing along a significant focus on
Influence Maximisation (IM) on social networks due to its wide applicability in
target advertising, viral marketing, and personalized recommendations. The aim
of IM is to identify certain nodes in the network which can help maximize the
spread of certain information through a diffusion cascade. While several works
have been proposed for IM, most were inefficient in exploiting community
structures to their full extent. In this work, we propose a community
structures-based approach, which employs a K-Shell algorithm in order to
generate a score for the connections between seed nodes and communities for
low-budget scenarios. Further, our approach employs entropy within communities
to ensure the proper spread of information within the communities. We choose
the Independent Cascade (IC) model to simulate information spread and evaluate
it on four evaluation metrics. We validate our proposed approach on eight
publicly available networks and find that it significantly outperforms the
baseline approaches on these metrics, while still being relatively efficient.
