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Social media networks are increasingly crucial in our lives, demanding deeper exploration of their dynamics. With billions of users and constant updates, modeling their complexity is challenging. Agent-based modeling (ABM) is a common approach to understanding social network communities, enabling individual behavior definition and system-level simulation. ABM is a potent tool for testing algorithmic impacts on user behavior. Leveraging ABM requires robust data processing and storage capabilities, where High Performance Computing (HPC) excels, efficiently handling complex computations. Machine Learning (ML) methods analyze vast social media data, enlightening user behaviors, preferences, and trends. Our proposal integrates ML to characterize user attributes and develop a comprehensive user model for ABM simulations in social networks on HPC systems.

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Applying Machine Learning for Social Networks Agent-Based Models

  • 会议论文

  • 发表日期：

    2025年

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  • Haoyuan Li,
  • Lidia Conde Matos,
  • Eduardo César Galobardes,
  • Anna Sikora

摘要

Social media networks are increasingly crucial in our lives, demanding deeper exploration of their dynamics. With billions of users and constant updates, modeling their complexity is challenging. Agent-based modeling (ABM) is a common approach to understanding social network communities, enabling individual behavior definition and system-level simulation. ABM is a potent tool for testing algorithmic impacts on user behavior. Leveraging ABM requires robust data processing and storage capabilities, where High Performance Computing (HPC) excels, efficiently handling complex computations. Machine Learning (ML) methods analyze vast social media data, enlightening user behaviors, preferences, and trends. Our proposal integrates ML to characterize user attributes and develop a comprehensive user model for ABM simulations in social networks on HPC systems.