  The deployment of modern network applications is increasing the network size
and traffic volumes at an unprecedented pace. Storing network-related
information (e.g., traffic traces) is key to enable efficient network
management. However, this task is becoming more challenging due to the
ever-increasing data transmission rates and traffic volumes. In this paper, we
present a novel method for network traffic compression that exploits spatial
and temporal patterns naturally present in network traffic. We consider a
realistic scenario where traffic measurements are performed at multiple links
of a network topology using tools like SNMP or NetFlow. Such measurements can
be seen as multiple time series that exhibit spatial and temporal correlations
induced by the network topology, routing or user behavior. Our method leverages
graph learning methods to effectively exploit both types of correlations for
traffic compression. The experimental results show that our solution is able to
outperform GZIP, the \textit{de facto} traffic compression method, improving by
50\%-65\% the compression ratio on three real-world networks.
