  In this work, we propose a new, fast and scalable method for anomaly
detection in large time-evolving graphs. It may be a static graph with dynamic
node attributes (e.g. time-series), or a graph evolving in time, such as a
temporal network. We define an anomaly as a localized increase in temporal
activity in a cluster of nodes. The algorithm is unsupervised. It is able to
detect and track anomalous activity in a dynamic network despite the noise from
multiple interfering sources. We use the Hopfield network model of memory to
combine the graph and time information. We show that anomalies can be spotted
with a good precision using a memory network. The presented approach is
scalable and we provide a distributed implementation of the algorithm. To
demonstrate its efficiency, we apply it to two datasets: Enron Email dataset
and Wikipedia page views. We show that the anomalous spikes are triggered by
the real-world events that impact the network dynamics. Besides, the structure
of the clusters and the analysis of the time evolution associated with the
detected events reveals interesting facts on how humans interact, exchange and
search for information, opening the door to new quantitative studies on
collective and social behavior on large and dynamic datasets.
