  Broad spectrum of urban activities including mobility can be modeled as
temporal networks evolving over time. Abrupt changes in urban dynamics caused
by events such as disruption of civic operations, mass crowd gatherings,
holidays and natural disasters are potentially reflected in these temporal
mobility networks. Identification and early detecting of such abnormal
developments is of critical importance for transportation planning and
security. Anomaly detection from high dimensional network data is a challenging
task as edge level measurements often have low values and high variance
resulting in high noise-to-signal ratio. In this study, we propose a generic
three-phase pipeline approach to tackle curse of dimensionality and noisiness
of the original data. Our pipeline consists of i) initial network aggregation
leveraging community detection ii) unsupervised dimensionality reduction iii)
clustering of the resulting representations for outlier detection. We perform
extensive experiments to evaluate the proposed approach on mobility data
collected from two major cities, New York City and Taipei. Our results
empirically prove that proposed methodology outperforms traditional approaches
for anomaly detection. We further argue that the proposed anomaly detection
framework is potentially generalizable to various other types of temporal
networks e.g. social interactions, information propagation and epidemic spread.
