将网络主题作为高阶相互作用建模:一种基于统计推理的方法

IF 1.9 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Frontiers in Physics Pub Date : 2024-08-27 DOI:10.3389/fphy.2024.1429731
Anatol E. Wegner
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引用次数: 0

摘要

流行的主题分析方法旨在通过识别子图模式来描述网络的局部连接结构,这些子图模式在网络中出现的频率明显高于在保留原始网络某些特征的空模型下的预期。在本文中,我们主张采用另一种基于生成模型统计推理的方法,在生成模型中,节点不仅通过边连接,还通过高阶子图的副本连接。这些模型自然会导致对潜在状态的考虑,而潜在状态对应于将网络分解为子图形式的高阶交互,子图可以具有任何简单连接图案的拓扑结构。该方法以解析原则为基础,可以从数千个候选主题中推断出简明的主题集,从而对更大的主题进行一致的检测。这种推论方法不仅能得到一组具有统计意义的高阶主题,还能将网络明确分解为这些主题,从而为系统研究网络中高阶连接结构的拓扑学和动力学意义提供了新的可能性。在简要回顾了核心概念和方法之后,我们提供了应用于实证数据集的示例,讨论了推理方法如何解决目前图案分析中的问题,并探讨了图案分析中常见的概念和方法如何转化为推理框架。
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Modelling network motifs as higher order interactions: a statistical inference based approach
The prevalent approach to motif analysis seeks to describe the local connectivity structure of networks by identifying subgraph patterns that appear significantly more often in a network then expected under a null model that conserves certain features of the original network. In this article we advocate for an alternative approach based on statistical inference of generative models where nodes are connected not only by edges but also copies of higher order subgraphs. These models naturally lead to the consideration of latent states that correspond to decompositions of networks into higher order interactions in the form of subgraphs that can have the topology of any simply connected motif. Being based on principles of parsimony the method can infer concise sets of motifs from within thousands of candidates allowing for consistent detection of larger motifs. The inferential approach yields not only a set of statistically significant higher order motifs but also an explicit decomposition of the network into these motifs, which opens new possibilities for the systematic study of the topological and dynamical implications of higher order connectivity structures in networks. After briefly reviewing core concepts and methods, we provide example applications to empirical data sets and discuss how the inferential approach addresses current problems in motif analysis and explore how concepts and methods common to motif analysis translate to the inferential framework.
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来源期刊
Frontiers in Physics
Frontiers in Physics Mathematics-Mathematical Physics
CiteScore
4.50
自引率
6.50%
发文量
1215
审稿时长
12 weeks
期刊介绍: Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.
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