Prediction and Intervention Strategy Design on Temporal Networks

Abstract

Communications networks such as opportunistic mobile networks, vehicle networks, and social contact networks can be represented as temporal networks, where two cars or individuals are connected at a discrete time step if they are close to each other or interact with each other at that time step. These networks facilitate the propagation of information or epidemics where a piece of information or a virus is transmitted from one node to another node through their time-varying contacts. Since the spread of misinformation or disease can lead to disruptions in social stability and hinder economic development, effective mitigation strategies can serve as a safeguard against potential losses. In this thesis, we pursue the challenge of enabling an effective intervention in spreading processes on temporal networks. The effectiveness of existing mitigation strategies critically depends on the insight into the effect of the intervention on the dynamic process in the future, which depends on the future dynamics of a complex system itself. However, in reality, a future topology of the temporal networks underlying those spreading processes is unknown. Hence, we first design methods to predict temporal networks in the future and also provide insightful interpretation regarding which inherent properties of temporal networks or mechanisms of network formation to consider when generating these predictions. For this prediction problem, we focus on network-based prediction models due to their general advantages, namely low computational complexity and analytic insights into the mechanisms that influence the evolution of the temporal network. Given that the development of such methods needs an understanding of patterns underlying the temporal networks that possibly facilitate the prediction, we start with investigating two interpretable learning algorithms i.e., Lasso Regression and Random Forest for temporal network prediction. We find that the previous activity of the target link itself contributes more than that of the neighboring links in predicting the target link’s future activity. Equipped with such insights, we then design network-based methods to predict temporal networks. Additionally, predicting temporal networks like physical contact networks in the long term future beyond the short-term i.e., one step ahead is crucial to forecast and mitigate the spread of epidemics and misinformation on the network. This long-term prediction problem has been seldom explored. Therefore, we propose basic methods that adapt each aforementioned prediction model to address classic short-term network prediction problems for long-term network prediction tasks. Assuming the temporal network in the future is perfectly predicted or known, we further design advanced strategies beyond the current state-of-the-art to effectively mitigate the spreading process on temporal networks. We investigate the intervention in information transport on a temporal network to link removal. The objective is to understand the removal of which types of links negatively influence the efficiency of information transport the most. Identifying such critical links via their properties will enable better protection (intervention) to facilitate (prohibit) the spread of (mis)information...