Targeted Influence Maximization Based on Cloud Computing over Big Data in Social Networks - Joshi, Kavita; Golhar, Roshani S.; Sathawane, Neerajkumar; Mathur, Ashutosh; Gawande, Pravin Ganpatrao; Patil, Milind S.; Gore, Santosh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research focuses on cloud computing-based targeted impact maximization in social networks. Most influence maximisation operates currently in use to identify the Top-k, which is a node in a network that is recognised or chosen according to specific standards, including the parameter "k." Users are expected to maximise the spread of influence under the assumption that the effect diffusion possibilities on connections are fixed, and these works assume an understanding of the whole networking graph. In practical settings, however, marginal probability tends to vary depending on a range of issues and can be influenced by incoming information. Therefore, the greedy algorithm is used. These approaches aim to detect a seed collection that increases the anticipated impact distribution across users with target audiences who are pertinent for specified subjects. The MIA model is used to locate the subgraph in a network where a certain collection of nodes can have the greatest impact on other nodes, which results in influence coverage and effectiveness. In the meantime, privacy and computational concerns make it challenging to access all network data. Additionally, current impact maximization techniques that take target users into account do not address cloud computing, which results in our algorithm consistently outperforming other scalable heuristics in influence spread across all size ranges, outperforming greedy algorithms by up to 100%-260%. This study suggests a cloud-based targeted influence maximization strategy to achieve this goal.