  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.
