Time series analysis of temporal networks

TL;DR

This paper introduces a method to predict future properties of temporal networks by converting them into time series and applying forecasting models, with applications in targeted attack strategies.

Contribution

It proposes a novel approach to estimate future network properties from time series analysis, including spectrogram-based refinement, for dynamic networks.

Findings

ARIMA models effectively capture the stochastic process of face-to-face contact networks.

Spectrogram analysis helps identify high-error prediction cases.

Prediction accuracy improved by approximately 8% with spectral refinement.

Abstract

An important feature of all real-world networks is that the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. We mainly focus on the temporal network of human face- to-face contacts and observe that it represents a stochastic process with memory that can be modeled as ARIMA. We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level <= 20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks.