Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends. In particular, the scalability of graph analysis is a critical hurdle impeding progress in large-scale downstream inference. To address this challenge, we introduce a temporal encoder embedding method. This approach leverages ground-truth or estimated vertex labels, enabling an efficient embedding of large-scale graph data and the processing of billions of edges within minutes. Furthermore, this embedding unveils a temporal dynamic statistic capable of detecting communication pattern shifts across all levels, ranging from individual vertices to vertex communities and the overall graph structure. We provide theoretical support to confirm its soundness under random graph models, and demonstrate its numerical advantages in capturing evolving communities and identifying outliers. Finally, we showcase the practical application of our approach by analyzing an anonymized time-series communication network from a large organization spanning 2019-2020, enabling us to assess the impact of Covid-19 on workplace communication patterns.

翻译：分析大规模时间序列网络数据（如社交媒体和电子邮件通信），对于理解社会动态、检测异常和预测趋势构成重大挑战。其中，图分析的可扩展性是阻碍大规模下游推理进展的关键障碍。为解决这一问题，我们引入了一种时序编码器嵌入方法。该方法利用真实或估计的顶点标签，能够高效嵌入大规模图数据，并在数分钟内处理数十亿条边。此外，这种嵌入揭示了一种时间动态统计量，可检测从单个顶点到顶点社区乃至整体图结构等所有层面的通信模式转变。我们提供了理论支撑，以确认其在随机图模型下的合理性，并展示了其在捕捉演化社区和识别异常值方面的数值优势。最后，通过分析某大型组织2019-2020年间的匿名化时间序列通信网络，我们展示了该方法在评估新冠疫情对工作场所通信模式影响方面的实际应用。