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<p>Social media are valuable tools for content generation, knowledge exchange, and interaction. Therefore, discovering influential users or seed nodes is critical or important when it comes to enhancing information diffusion in OSNs. In this paper, we propose the IDT-Cascade model, a way to infer distinctive cascades relying on structural characteristics, starring the height of trees of information diffusion. The model incorporates a tool in Python that identifies and analyses Information cascades by using Degree centrality and Breadth-first search (BFS). Going by the information in the tables above, a threshold defined on the basis of average cascade height is suggested for declaring influential cascades. The model was then tested using real-world networks such as the Student Government, Galesburg, and Strike structure to show that it could identify significant entertainment cascades. For example, out of two cascades included in the Student Government set, we determined that two of them were influential while demonstrating the suggested model’s applicability. As a result, the proposed IDT-Cascade model is a highly efficient, computationally tractable approach to cascade detection with implications for improving communication plans in organizational and educational settings. This work contributes to the literature by presenting improvements over current methods of cascade modeling as well as offering a reliable apparatus for studying social networks.</p>

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IDT-Cascade: a novel information dissemination tree model for influential cascade detection in online social networks

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Aaquib Hussain Ganai,
  • Rana Hashmy,
  • Hilal Ahmad Khanday,
  • Monica Bhutani

摘要

Social media are valuable tools for content generation, knowledge exchange, and interaction. Therefore, discovering influential users or seed nodes is critical or important when it comes to enhancing information diffusion in OSNs. In this paper, we propose the IDT-Cascade model, a way to infer distinctive cascades relying on structural characteristics, starring the height of trees of information diffusion. The model incorporates a tool in Python that identifies and analyses Information cascades by using Degree centrality and Breadth-first search (BFS). Going by the information in the tables above, a threshold defined on the basis of average cascade height is suggested for declaring influential cascades. The model was then tested using real-world networks such as the Student Government, Galesburg, and Strike structure to show that it could identify significant entertainment cascades. For example, out of two cascades included in the Student Government set, we determined that two of them were influential while demonstrating the suggested model’s applicability. As a result, the proposed IDT-Cascade model is a highly efficient, computationally tractable approach to cascade detection with implications for improving communication plans in organizational and educational settings. This work contributes to the literature by presenting improvements over current methods of cascade modeling as well as offering a reliable apparatus for studying social networks.