用图神经网络分析手机连接数据，预测用户间通话与短信行为。
Temporal social network modeling of mobile connectivity data with graph neural networks
- 采用四种基于快照的时序GNN模型建模移动通信网络
- ROLAND GNN在多数情况下优于基线模型，其他GNN表现更差
- 适合对时序社交网络建模感兴趣的学者和应用开发者
图神经网络（GNN）已成为建模图结构复杂网络连接数据的前沿数据驱动工具，能整合节点与边在时空中的信息。然而，利用人移动连接数据的时间序列分析社交网络的研究仍不充分。本研究考察了四种基于快照的时序GNN模型，用于预测移动通信网络中用户间的电话呼叫与短信活动。此外，我们构建了一个基于近期提出的EdgeBank方法的简单非GNN基线模型。结果表明，ROLAND时序GNN在多数情况下优于基线模型，而其余三种GNN平均表现更差。研究显示，基于GNN的方法在通过移动连接数据分析时序社交网络方面具有潜力，但因ROLAND与基线模型性能差距较小，仍需针对时序社交网络分析设计专用的GNN架构。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.
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