Abstract :

Influence Maximization (IM) Has Emerged As A Cornerstone Problem In Social Network Analysis, Focusing On Identifying Key Nodes Capable Of Maximizing The Spread Of Information, Behaviors, Or Innovations Through A Network. With The Evolution Of Social Ecosystems Ranging From Microblogging Platforms To Dynamic Online Communities Traditional IM Algorithms Based On Independent Cascade (IC) And Linear Threshold (LT) Models Face Limitations In Scalability, Adaptability, And Interpretability. This Paper Revisits The IM Paradigm Through The Lens Of Adaptive, Explainable, And Fairness-aware Optimization. It Reviews Algorithmic Advancements Between 2021 And 2025, Including Reinforcement Learning (RL)-based Adaptive Seeding, Graph Neural Network (GNN)-driven Diffusion Prediction, Fairness-constrained Diffusion Strategies, And Hybrid Metaheuristic Approaches. A Novel Taxonomy Is Proposed, Classifying Algorithms Based On Adaptability, Transparency, And Ethical Awareness. Comparative Analyses Highlight Trade-offs Among Computational Complexity, Influence Gain, And Social Equity. The Study Concludes With Key Research Challenges In Data Uncertainty, Adversarial Robustness, And Ethical Governance, Outlining Opportunities For Quantum-inspired And Privacy-preserving Frameworks. By Bridging Algorithmic Evolution With Real-world Applicability, This Work Provides A Unified Reference For Researchers And Practitioners In Influence Analytics, Social Computing, And Network Optimization.

Published:

12-10-2024

Issue:

Vol. 24 No. 10 (2024)

Page Nos:

475-485

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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