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...@en

Temporal networks are networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast and mitigate the spread of epidemics and misinformation on the network. Most existing methods for temporal network prediction are based on machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. This motivates us to develop network-based models to predict the temporal network at the next time step based on the network observed in the past. Firstly, we investigate temporal network properties to motivate our network prediction models and to explain how the performance of these models depends on the temporal networks. We explore the similarity between the network topology (snapshot) at any two time steps with a given time lag/interval. We find that the similarity is relatively high when the time lag is small and decreases as the time lag increases. Inspired by such time-decaying memory of temporal networks and recent advances, we propose two models that predict a link’s future activity (i.e., connected or not), based on the past activities of the link itself or also of neighboring links, respectively. Via seven real-world physical contact networks, we find that our models outperform in both prediction quality and computational complexity, and predict better in networks that have a stronger memory. Beyond, our model also reveals how different types of neighboring links contribute to the prediction of a given link’s future activity, again depending on properties of temporal networks.

@en

Temporal networks are networks whose topology changes over time. Two nodes in a temporal network are connected at a discrete time step only if they have a contact/interaction at that time. The classic temporal network prediction problem aims to predict the temporal network one time step ahead based on the network observed in the past of a given duration. This problem has been addressed mostly via machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. Hence, we propose to predict the connection of each node pair one step ahead based on the connections of this node pair itself and of node pairs that share a common node with this target node pair in the past. The concrete design of our two prediction models is based on the analysis of the memory property of real-world physical networks, i.e., to what extent two snapshots of a network at different times are similar in topology (or overlap). State-of-the-art prediction methods that allow interpretation are considered as baseline models. In seven real-world physical contact networks, our methods are shown to outperform the baselines in both prediction accuracy and computational complexity. They perform better in networks with stronger memory. Importantly, our models reveal how the connections of different types of node pairs in the past contribute to the connection estimation of a target node pair. Predicting temporal networks like physical contact networks in the long-term future beyond 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 problem for long-term network prediction task. The prediction quality of all adapted models is evaluated via the accuracy in predicting each network snapshot and in reproducing key network properties. The prediction based on one of our models tends to have the highest accuracy and lowest computational complexity.

@en

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods motivates us to explore what intrinsic properties/mechanisms facilitate the prediction of temporal networks. We use interpretable learning algorithms, Lasso Regression and Random Forest, to predict, based on the current activities (i.e., connected or not) of all links, the activity of each link at the next time step. From the coefficients learned from each algorithm, we construct the prediction backbone network that presents the influence of all links in determining each links future activity. Analysis of the backbone, its relation to the link activity time series and to the time aggregated network reflects which properties of temporal networks are captured by the learning algorithms. Via six real-world contact networks, we find that the next step activity of a particular link is mainly influenced by (a) its current activity and (b) links strongly correlated in the time series to that particular link and close in distance (in hops) in the aggregated network.

@en