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<p>The rapid growth of online social networks has intensified interest in the Influence Maximization (IM) problem, which seeks to identify a small set of users capable of maximizing information diffusion across a network. This paper presents a comprehensive survey of IM methodologies, provides a refined taxonomy that classifies algorithms into approximate, heuristic, metaheuristic, and community-based categories. This work integrates both classical and recent advancements, including diversified and dynamic IM techniques as well as community-driven and topology-based approaches. This paper consolidates perspectives from recent Social Network Analysis (SNA) frameworks, including process-based, quantum, and privacy-aware studies, to provide a holistic understanding of influence propagation. Each category is critically analyzed concerning scalability, influence spread, computational complexity, and application scope. Furthermore, the review highlights emerging domains where IM plays a vital role such as educational analytics, public health communication, and criminal network analysis demonstrating its interdisciplinary impact. Finally, the paper outlines major research challenges and future directions, including dynamic network modeling, privacy-preserving diffusion, and quantum and deep learning integration. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking to develop scalable, ethical, and context-aware IM strategies in complex social systems.</p>

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A Survey on Influence Maximization: Past Contribution and Future Roadmap

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • M Venunath,
  • Pothula Sujatha,
  • V. Subhashini,
  • Murali Krishna Puttagunta,
  • T Chandra Sekhar Rao

摘要

The rapid growth of online social networks has intensified interest in the Influence Maximization (IM) problem, which seeks to identify a small set of users capable of maximizing information diffusion across a network. This paper presents a comprehensive survey of IM methodologies, provides a refined taxonomy that classifies algorithms into approximate, heuristic, metaheuristic, and community-based categories. This work integrates both classical and recent advancements, including diversified and dynamic IM techniques as well as community-driven and topology-based approaches. This paper consolidates perspectives from recent Social Network Analysis (SNA) frameworks, including process-based, quantum, and privacy-aware studies, to provide a holistic understanding of influence propagation. Each category is critically analyzed concerning scalability, influence spread, computational complexity, and application scope. Furthermore, the review highlights emerging domains where IM plays a vital role such as educational analytics, public health communication, and criminal network analysis demonstrating its interdisciplinary impact. Finally, the paper outlines major research challenges and future directions, including dynamic network modeling, privacy-preserving diffusion, and quantum and deep learning integration. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking to develop scalable, ethical, and context-aware IM strategies in complex social systems.