Temporal correlation-based neural relational inference for binary dynamics

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Haowei Zhang , Yuexing Han , Gouhei Tanaka , Bing Wang
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引用次数: 0

Abstract

Binary-state dynamics are prevalent in nature, from societal dynamics to dynamical systems in physics. Reconstructing a network structure behind interacting binary-state dynamical systems is essential, as it can facilitate understanding of these dynamical systems and improve the accuracy of predicting dynamical behavior. So far, few works have focused on correlation information in binary-state temporal data to help reconstruct networks. In this study, we propose temporal correlation-based neural relational inference for binary dynamics (TCNRI), inspired by the maximum likelihood estimation of activation events in binary dynamics processes. TCNRI constructs instantaneous correlation features and long-term correlation features by analyzing activation events in the time series data. These features capture the correlation information and help TCNRI reconstruct the network structure. We treat the binary-state dynamical process as a Markov process and use neural networks to reproduce node dynamics based on the reconstructed network structure. We conduct simulations on the classic susceptible–infected–susceptible (SIS) dynamics and Ising dynamics. The results show that TCNRI significantly outperforms baseline models and can accurately reconstruct the network structure for both typical synthetic networks and real networks.
二元动力学中基于时间相关的神经关系推理
二态动力学在自然界中很普遍,从社会动力学到物理学中的动力系统。重建相互作用二态动力系统背后的网络结构是必要的,因为它可以促进对这些动力系统的理解,提高预测动力行为的准确性。到目前为止,很少有研究关注二值态时间数据中的相关信息来帮助重建网络。在这项研究中,我们提出了基于时间相关的二元动力学神经关系推理(TCNRI),灵感来自于二元动力学过程中激活事件的最大似然估计。TCNRI通过分析时间序列数据中的激活事件,构建瞬时相关特征和长期相关特征。这些特征捕获相关信息,帮助TCNRI重建网络结构。我们将二值状态动态过程视为马尔可夫过程,并利用神经网络在重构网络结构的基础上再现节点动态。我们对经典的易感-感染-易感(SIS)动力学和伊辛动力学进行了模拟。结果表明,TCNRI显著优于基线模型,可以准确地重建典型合成网络和真实网络的网络结构。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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