基于深度强化学习的具有动态级联故障的超图成本约束分解框架

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Ting Jiang , Kai Liu , Bing-Bing Xiang , Hai-Feng Zhang , Huan Wang
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引用次数: 0

摘要

拆除网络是复杂网络研究中的一项基本挑战,具有重要的现实应用,如控制疾病传播和破坏恐怖主义网络。然而,大多数现有的研究都集中在两两交互网络和静态拆除策略上,往往忽视了级联故障的动态性和相关的成本约束,这在现实世界中是普遍存在的。这些限制限制了它们在具有高阶相互作用的系统中有效捕获失效动力学复杂性的能力。为了克服这些挑战,我们利用超图来模拟复杂系统中的高阶关系,并引入一种动态分解方法,该方法明确地解释了成本约束下的级联故障。在此基础上,我们提出了一种新的超图分解框架,C2HD-RL,它利用了深度强化学习。该框架使智能体能够迭代地探索合成超图中不同的节点选择策略,并根据所获得的奖励调整其行为,最终学习到最优的超图拆除策略。对九个真实世界超图数据集的综合评估,与七个基线方法进行比较,证明了我们的方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A cost-constrained dismantling framework for hypergraph with dynamic cascading failure using deep reinforcement learning
Network dismantling is a fundamental challenge in the study of complex networks, with significant real-world applications such as controlling the spread of diseases and disrupting terrorist networks. However, most existing studies focus on pairwise interaction networks and static dismantling strategies, often overlooking the dynamic nature of cascading failures and the associated cost constraints that are prevalent in real-world scenarios. These limitations restrict their ability to effectively capture the complexity of failure dynamics in systems with higher-order interactions. To overcome these challenges, we utilize hypergraphs to model higher-order relationships within complex systems and introduce a dynamic dismantling approach that explicitly accounts for cascading failures under cost constraints. Building on this foundation, we propose a novel hypergraph dismantling framework, C2HD-RL, which leverages deep reinforcement learning. The framework enables an agent to iteratively explore different node selection strategies in synthetic hypergraphs and adjust its behavior based on the rewards received, ultimately learning an optimal hypergraph dismantling strategy. Comprehensive evaluations on nine real-world hypergraph datasets, compared against seven baseline methods, demonstrate the effectiveness of our approach.
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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