An Influence Maximization in Social Networks Based on Community Structure (IMSC)

Recognition of the top k users (seed) in social networks is a challenge in influence maximization (IM), which aims to increase influence propagation.Finding the network's most important nodes can help network analysts track the spread of rumors, diseases, and data in many applications.We provide the Influence Maximization in Social Networks Based on Community Structure (IMSC) technique to address this problem.The three phases of our suggested framework, IMSC, are as follows: the network's community structure is identified, candidates are generated using community information, and seed nodes are finally selected from the candidate set.In this phase, the IMSC method looks at a network or group of interconnected things (like social connections or data points).It determines how they naturally form smaller communities or groups.After identifying these groups, the method generates a list of potential candidates or members who could play a key role in spreading data or influence within each community.Finally, from the list of potential candidates, the method selects a few critical individuals called "seed nodes."These seed nodes are starting points for spreading information or influence throughout the network.Our tests on real-world datasets display that the proposed method surpasses the competition in terms of the value of the corresponding output influential nodes while still using an acceptable amount of memory and processing time for massive graphs.We assess our algorithm against state-of-the-art solutions to the influence maximization issue.