Influence maximization in social networks aims to identify a set of key users who can effectively spread information or influence across the network. However, selecting influential users often involves high computational complexity and cost. This paper presents PP Rank, an economic and efficient approach for selecting initial users to maximize influence spread while minimizing resource usage. The proposed method prioritizes potential users based on propagation probability, network structure, and interaction strength, allowing for balanced trade-offs between influence gain and selection cost. By integrating probabilistic modeling and ranking algorithms, PP Rank identifies a cost-effective subset of users that ensures wide information diffusion. Experimental evaluations on real-world social network datasets demonstrate that PP Rank achieves near-optimal influence coverage with significantly reduced computational time compared to traditional greedy or heuristic-based methods. This study contributes to the design of scalable, economical influence maximization strategies applicable in viral marketing, information dissemination, and social recommendation systems.
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