标题： 应用机器学习对基于代理的社会网络模型进行特征刻画 显示英文标题

标题： Applying Machine Learning for characterizing social networks Agent-based models

摘要： 如今，社交媒体网络在我们的生活中变得越来越重要，研究这些网络的紧迫性也变得越来越重要。平台上拥有数十亿用户并且不断更新，这使得建模社交网络的复杂性极大。 基于智能体的建模（ABM）被广泛用于研究社交网络社区，使我们能够定义个体行为并模拟系统层面的演变。它可以用作测试算法如何影响用户行为的强大工具。 为了充分利用基于智能体的模型，需要优秀的数据处理和存储能力。高性能计算（HPC）提供了一个最优解决方案，擅长管理复杂的计算和分析，特别是在处理大量或迭代密集型任务方面。 由于机器学习（ML）方法能够高效地处理大量数据并从中提取洞见以帮助理解用户行为、偏好和趋势，我们利用这些方法来分析社交媒体用户。 因此，我们的建议是使用ML来刻画用户属性，并为在HPC系统上进行社交网络的ABM仿真开发一个通用用户模型。 显示英文摘要

摘要： Nowadays, social media networks are increasingly significant to our lives, the imperative to study social media networks becomes more and more essential. With billions of users across platforms and constant updates, the complexity of modeling social networks is immense. Agent-based modeling (ABM) is widely employed to study social networks community, allowing us to define individual behaviors and simulate system-level evolution. It can be a powerful tool to test how the algorithms affect users behavior. To fully leverage agent-based models,superior data processing and storage capabilities are essential. High Performance Computing (HPC) presents an optimal solution, adept at managing complex computations and analysis, particularly for voluminous or iteration-intensive tasks. We utilize Machine Learning (ML) methods to analyze social media users due to their ability to efficiently process vast amounts of data and derive insights that aid in understanding user behaviors, preferences, and trends. Therefore, our proposal involves ML to characterize user attributes and to develop a general user model for ABM simulation of in social networks on HPC systems.