Short- and long-term temporal network prediction based on network memory

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS
Li Zou, Alberto Ceria, Huijuan Wang
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引用次数: 0

Abstract

Abstract 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.
基于网络记忆的短期和长期网络预测
时态网络是指拓扑结构随时间变化的网络。时间网络中的两个节点只有在有接触/交互时才在离散时间步长连接。经典的时间网络预测问题的目的是在过去一段时间内观测到的网络的基础上,提前一步预测时间网络。这个问题主要是通过机器学习算法来解决的,代价是高昂的计算成本和对构成网络的底层机制的有限解释。因此,我们建议根据每个节点对本身的连接以及过去与该目标节点对共享一个公共节点的节点对的连接,提前一步预测每个节点对的连接。我们的两个预测模型的具体设计是基于对现实世界物理网络的内存特性的分析,即,在不同时间的网络的两个快照在拓扑上相似(或重叠)的程度。允许解释的最先进的预测方法被认为是基线模型。在七个现实世界的物理接触网络中,我们的方法在预测精度和计算复杂度方面都优于基线。他们在记忆力强的网络中表现更好。重要的是,我们的模型揭示了过去不同类型节点对的连接如何有助于目标节点对的连接估计。预测长期未来的时间网络,如物理接触网络,而不是短期的,即提前一步,对于预测和减轻网络上流行病和错误信息的传播至关重要。这个长期预测问题很少被探讨。因此,我们提出了将上述各种预测模型进行调整的基本方法,以解决经典的短期网络预测问题,实现长期网络预测任务。通过预测每个网络快照和再现关键网络属性的准确性来评估所有适应模型的预测质量。基于我们其中一个模型的预测往往具有最高的准确性和最低的计算复杂度。
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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来源期刊
Applied Network Science
Applied Network Science Multidisciplinary-Multidisciplinary
CiteScore
4.60
自引率
4.50%
发文量
74
审稿时长
5 weeks
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