用图形神经网络对移动连接数据进行时间社会网络建模
Joel Jaskari1, Chandreyee Roy1, Fumiko Ogushi2
1Department of Computer Science, Aalto University, Aalto, Finland.
PloS one
|December 10, 2025
概括
图形神经网络 (GNN) 显示出使用移动数据分析时间社会网络的前景. 罗兰德GNN模型的性能优于基线,但对于专门的架构需要进一步研究.
科学领域:
- 复杂网络分析 复杂网络分析
- 机器学习应用程序 机器学习应用程序
- 社交网络建模模型
背景情况:
- 图形神经网络 (GNN) 擅长模拟复杂的网络连接.
- 使用移动连接数据进行时间社会网络分析尚未得到充分探索.
- 预测移动网络中的用户活动对于理解社会动态至关重要.
研究的目的:
- 评估基于快照的时间GNN,用于预测移动通信活动.
- 将GNN性能与非GNN基线 (EdgeBank) 进行比较.
- 评估GNN在时间社会网络分析中的潜力.
主要方法:
- 研究了四个基于快照的时间GNN模型.
- 使用EdgeBank方法开发了一个非GNN基线.
- 分析了来自移动网络的电话和短信活动数据.
主要成果:
- 在大多数场景中,ROLAND时间GNN模型在基线上表现出优越的性能.
- 其他三种时间GNN模型的平均表现比基线差.
- 基于GNN的方法显示了对时间社会网络分析的潜力.
结论:
- 通过移动连接数据分析时间社交网络,GNN具有前景.
- 罗兰GNN是一个有前途的架构,性能超过了基线.
- 由于性能边际较小,需要对专门的GNN架构进行进一步的研究.
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