Online network monitoring

TL;DR

This paper introduces a real-time network monitoring method combining network modeling with statistical process control, effectively detecting anomalies in complex temporal networks like daily US flights.

Contribution

It presents a novel approach integrating TERGM with multivariate control charts for online anomaly detection in dynamic networks.

Findings

Effective detection of anomalies in simulated data

Successful application to US flight data

Reduced parameter complexity in monitoring

Abstract

The application of network analysis has found great success in a wide variety of disciplines; however, the popularity of these approaches has revealed the difficulty in handling networks whose complexity scales rapidly. One of the main interests in network analysis is the online detection of anomalous behaviour. To overcome the curse of dimensionality, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks determined by temporal exponential random graph models (TERGM). This allows us to account for temporal dependence, while simultaneously reducing the number of parameters to be monitored. The performance of the proposed charts is evaluated by calculating the average run length for both simulated and real data. To prove the appropriateness of the TERGM to describe network data, some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns.