  There have been several recent efforts towards developing representations for
multivariate time-series in an unsupervised learning framework. Such
representations can prove beneficial in tasks such as activity recognition,
health monitoring, and anomaly detection. In this paper, we consider a setting
where we observe time-series at each node in a dynamic graph. We propose a
framework called GraphTNC for unsupervised learning of joint representations of
the graph and the time-series. Our approach employs a contrastive learning
strategy. Based on an assumption that the time-series and graph evolution
dynamics are piecewise smooth, we identify local windows of time where the
signals exhibit approximate stationarity. We then train an encoding that allows
the distribution of signals within a neighborhood to be distinguished from the
distribution of non-neighboring signals. We first demonstrate the performance
of our proposed framework using synthetic data, and subsequently we show that
it can prove beneficial for the classification task with real-world datasets.
