Higher-Order Temporal Network Prediction and Interpretation

H. A. Bart Peters, Alberto Ceria, Huijuan Wang
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Abstract

A social interaction (so-called higher-order event/interaction) can be regarded as the activation of the hyperlink among the corresponding individuals. Social interactions can be, thus, represented as higher-order temporal networks, that record the higher-order events occurring at each time step over time. The prediction of higher-order interactions is usually overlooked in traditional temporal network prediction methods, where a higher-order interaction is regarded as a set of pairwise interactions. The prediction of future higher-order interactions is crucial to forecast and mitigate the spread the information, epidemics and opinion on higher-order social contact networks. In this paper, we propose novel memory-based models for higher-order temporal network prediction. By using these models, we aim to predict the higher-order temporal network one time step ahead, based on the network observed in the past. Importantly, we also intent to understand what network properties and which types of previous interactions enable the prediction. The design and performance analysis of these models are supported by our analysis of the memory property of networks, e.g., similarity of the network and activity of a hyperlink over time respectively. Our models assume that a target hyperlink's future activity (active or not) depends the past activity of the target link and of all or selected types of hyperlinks that overlap with the target. We then compare the performance of both models with a baseline utilizing a pairwise temporal network prediction method. In eight real-world networks, we find that both models consistently outperform the baseline and the refined model tends to perform the best. Our models also reveal how past interactions of the target hyperlink and different types of hyperlinks that overlap with the target contribute to the prediction of the target's future activity.
高阶时态网络预测与解释
社会互动(所谓的高阶事件/互动)可视为相应个体之间超链接的激活。因此,社会互动可以用高阶时态网络来表示,它记录了随着时间推移在每个时间步发生的高阶事件。在传统的时态网络预测方法中,高阶互动被视为一组配对互动,而高阶互动的预测通常被忽视。预测未来的高阶交互对于预测和缓解高阶社会接触网络中的信息传播、流行病和舆论至关重要。在本文中,我们提出了基于记忆的新型高阶时态网络预测模型。通过使用这些模型,我们旨在根据过去观察到的网络,提前一个时间步预测高阶时态网络。重要的是,我们还希望了解是哪些网络属性和哪些类型的先前交互促成了预测。这些模型的设计和性能分析得益于我们对网络记忆特性的分析,例如网络的相似性和超链接随时间变化的活动性。我们的模型假设目标超链接的未来活动(活跃与否)取决于目标链接以及与目标链接重叠的所有或选定类型超链接的过去活动性。然后,我们利用成对时态网络预测方法将这两种模型的性能与基准线进行了比较。在八个真实世界的网络中,我们发现这两个模型的性能始终优于基准线,而改进后的模型往往表现最佳。我们的模型还揭示了目标超链接和与目标重叠的不同类型超链接过去的交互如何有助于预测目标的未来活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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