Characterizing the dynamics of unlabeled temporal networks.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-05-01 DOI:10.1063/5.0253870
Annalisa Caligiuri, Tobias Galla, Lucas Lacasa
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引用次数: 0

Abstract

Networks model the architecture backbone of complex systems. The backbone itself can change over time leading to what is called "temporal networks." Interpreting temporal networks as trajectories in graph space of a latent graph dynamics has recently enabled the extension of concepts and tools from dynamical systems and time series to networks. Here, we address temporal networks with unlabeled nodes, a case that has received relatively little attention so far. Situations in which node labeling cannot be tracked over time often emerge in practice due to technical challenges or privacy constraints. In unlabeled temporal networks, there is no one-to-one matching between a network snapshot and its adjacency matrix. Characterizing the dynamical properties of such unlabeled network trajectories is, therefore, nontrivial. Here, we exploit graph invariants to extend some recently proposed network-dynamical quantifiers of linear correlations and dynamical instability to the unlabeled setting. In particular, we focus on autocorrelation functions and the sensitive dependence on initial conditions. We show with synthetic graph dynamics that the measures are capable of recovering and estimating these dynamical fingerprints even when node labels are unavailable. We also validate the methods for some empirical temporal networks with removed node labels.

表征未标记的时间网络的动态。
网络为复杂系统的架构主干建模。主干网本身可以随着时间的推移而改变,从而形成所谓的“时间网络”。将时间网络解释为潜在图动力学的图空间中的轨迹,最近使概念和工具从动态系统和时间序列扩展到网络。在这里,我们讨论了具有未标记节点的时间网络,这种情况迄今为止受到的关注相对较少。由于技术挑战或隐私限制,在实践中经常出现节点标记无法随时间跟踪的情况。在未标记的时态网络中,网络快照与其邻接矩阵之间没有一对一的匹配。因此,描述这种未标记网络轨迹的动态特性是非平凡的。在这里,我们利用图不变量将一些最近提出的线性相关性和动态不稳定性的网络动态量词扩展到未标记的设置。我们特别关注自相关函数和对初始条件的敏感依赖。我们用合成图动力学表明,即使在节点标签不可用的情况下,这些度量也能够恢复和估计这些动态指纹。我们还验证了一些去除节点标签的经验时态网络的方法。
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
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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