Bart Peters H A, Ceria Alberto, Wang Huijuan
Delft University of Technology, Delft, The Netherlands.
Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands.
PLoS One. 2025 May 29;20(5):e0323753. doi: 10.1371/journal.pone.0323753. eCollection 2025.
A social interaction (so-called higher-order event/interaction) can be regarded as the activation of a 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 of 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 intend to understand what network properties and which types of previous interactions enable the prediction. The design and performance analysis of these models is 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 on 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 our models with three baseline models, which are an activity driven model, a probabilistic group-change model and a pairwise temporal network prediction method. In eight real-world networks, we find that both our models consistently outperform the baselines. Moreover, the refined model, which only uses a subset of all types of overlapping hyperlinks, 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.
社交互动（所谓的高阶事件/互动）可被视为相应个体之间超链接的激活。因此，社交互动可以表示为高阶时间网络，该网络记录了随着时间推移在每个时间步发生的高阶事件。在传统的时间网络预测方法中，高阶互动的预测通常被忽视，在这些方法中，高阶互动被视为一组成对互动。预测未来的高阶互动对于预测和减轻高阶社交联系网络上信息、流行病和观点的传播至关重要。在本文中，我们提出了用于高阶时间网络预测的基于记忆的新颖模型。通过使用这些模型，我们旨在根据过去观察到的网络预测提前一个时间步的高阶时间网络。重要的是，我们还希望了解哪些网络属性以及哪些类型的先前互动能够实现预测。这些模型的设计和性能分析得到了我们对网络记忆属性的分析的支持，例如，分别是网络的相似性和超链接随时间的活跃度。我们的模型假设目标超链接的未来活动（是否活跃）取决于目标链接以及与目标重叠的所有或选定类型超链接的过去活动。然后，我们将我们模型的性能与三个基线模型进行比较，这三个基线模型分别是活动驱动模型、概率组变化模型和成对时间网络预测方法。在八个真实世界网络中，我们发现我们的两个模型始终优于基线模型。此外，仅使用所有类型重叠超链接的一个子集的改进模型往往表现最佳。我们的模型还揭示了目标超链接以及与目标重叠的不同类型超链接的过去互动如何有助于预测目标的未来活动。
