Stability of large-scale probabilistic Boolean networks via network aggregation

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2025-09-11 DOI:10.1016/j.neunet.2025.108108

Wen Liu , Shihua Fu , Jianjun Wang , Renato De Leone , Jianwei Xia

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引用次数: 0

Abstract

Large-scale probabilistic Boolean networks (LSPBNs) are a modeling tool used to simulate and analyze the dynamics of complex systems with uncertainty. However, due to its high computational complexity, previous research methods cannot be directly applied to study such systems. Inspired by network aggregation, this paper conducts network aggregation on LSPBNs to investigate its global stability with probability 1. It is worth mentioning that the stability conclusion proposed in this article holds for any form of network aggregation. First, the entire network is partitioned and the algebraic expressions for each subnetwork are given through the semi-tensor product of matrices. And then, a set of iterative formulas is constructed to describe and reflect the input-output coordination relationship among the subnetworks, and based on which, a sufficient condition for the global stability of LSPBNs is derived, greatly reducing computational complexity. The feasibilities of the proposed method and results are verified through examples.

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基于网络聚合的大规模概率布尔网络的稳定性。

大规模概率布尔网络（lspbn）是一种用于模拟和分析具有不确定性的复杂系统动力学的建模工具。然而，由于其计算复杂度较高，以往的研究方法无法直接应用于此类系统的研究。受网络聚合的启发，本文在lspbn上进行网络聚合，研究其概率为1的全局稳定性。值得一提的是，本文提出的稳定性结论适用于任何形式的网络聚合。首先，对整个网络进行了划分，并通过矩阵的半张量积给出了各子网络的代数表达式。在此基础上，构造了一组描述和反映子网间输入-输出协调关系的迭代公式，并在此基础上推导了lspbn全局稳定的充分条件，大大降低了计算复杂度。通过算例验证了所提方法和结果的可行性。

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

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.