分数驱动的指数随机图:一类新的时态网络时变参数模型。

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2024-11-01 DOI:10.1063/5.0222079
D Di Gangi, G Bormetti, F Lillo
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引用次数: 0

摘要

描述真实世界网络的数据日益丰富,这些数据显示出动态特征,受此激励,我们提出了指数随机图模型(ERGMs)的扩展,以适应其参数的时间变化。受快速增长的动态条件得分模型文献的启发,每个参数都会根据 ERGM 分布得分驱动的更新规则发生变化。我们展示了分数驱动 ERGM(SD-ERGM)作为数据生成过程和过滤器的灵活性,并显示了动态版本相对于静态版本的优势。我们讨论了金融和政治系统时间网络的两个应用。首先,我们考虑预测意大利银行间信贷网络的未来联系。其次,我们展示了 SD-ERGM 在用于模拟美国国会共同投票网络动态时,可以区分静态参数和时变参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

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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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