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Deep-learning-aided dismantling of interdependent networks.

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Title: Deep-learning-aided dismantling of interdependent networks.
Authors: Gu, Weiwei, Yang, Chen, Li, Lei, Hou, Jinqiang, Radicchi, Filippo
Source: Nature Machine Intelligence; Aug2025, Vol. 7 Issue 8, p1266-1277, 12p
Abstract: Identifying the minimal set of nodes whose removal breaks a complex network apart, also referred as the network dismantling problem, is a highly non-trivial task with applications in multiple domains. Whereas network dismantling has been extensively studied over the past decade, research has primarily focused on the optimization problem for single-layer networks, neglecting that many, if not all, real networks display multiple layers of interdependent interactions. In such networks, the optimization problem is fundamentally different as the effect of removing nodes propagates within and across layers in a way that can not be predicted using a single-layer perspective. Here we propose a dismantling algorithm named MultiDismantler, which leverages multiplex network representation and deep reinforcement learning to optimally dismantle multilayer interdependent networks. MultiDismantler is trained on small synthetic graphs; when applied to large, either real or synthetic, networks, it displays exceptional dismantling performance, clearly outperforming all existing benchmark algorithms. We show that MultiDismantler is effective in guiding strategies for the containment of diseases in social networks characterized by multiple layers of social interactions. Also, we show that MultiDismantler is useful in the design of protocols aimed at delaying the onset of cascading failures in interdependent critical infrastructures. The dismantling problem of removing the smallest set of nodes so that a given network breaks into disconnected components is hard to solve exactly. Gu and colleagues use deep reinforcement learning and a multiplex network representation to avoid the heavy computational cost. [ABSTRACT FROM AUTHOR]
Copyright of Nature Machine Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Deep-learning-aided dismantling of interdependent networks.
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  Data: <searchLink fieldCode="AR" term="%22Gu%2C+Weiwei%22">Gu, Weiwei</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Chen%22">Yang, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Lei%22">Li, Lei</searchLink><br /><searchLink fieldCode="AR" term="%22Hou%2C+Jinqiang%22">Hou, Jinqiang</searchLink><br /><searchLink fieldCode="AR" term="%22Radicchi%2C+Filippo%22">Radicchi, Filippo</searchLink>
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  Data: Nature Machine Intelligence; Aug2025, Vol. 7 Issue 8, p1266-1277, 12p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Identifying the minimal set of nodes whose removal breaks a complex network apart, also referred as the network dismantling problem, is a highly non-trivial task with applications in multiple domains. Whereas network dismantling has been extensively studied over the past decade, research has primarily focused on the optimization problem for single-layer networks, neglecting that many, if not all, real networks display multiple layers of interdependent interactions. In such networks, the optimization problem is fundamentally different as the effect of removing nodes propagates within and across layers in a way that can not be predicted using a single-layer perspective. Here we propose a dismantling algorithm named MultiDismantler, which leverages multiplex network representation and deep reinforcement learning to optimally dismantle multilayer interdependent networks. MultiDismantler is trained on small synthetic graphs; when applied to large, either real or synthetic, networks, it displays exceptional dismantling performance, clearly outperforming all existing benchmark algorithms. We show that MultiDismantler is effective in guiding strategies for the containment of diseases in social networks characterized by multiple layers of social interactions. Also, we show that MultiDismantler is useful in the design of protocols aimed at delaying the onset of cascading failures in interdependent critical infrastructures. The dismantling problem of removing the smallest set of nodes so that a given network breaks into disconnected components is hard to solve exactly. Gu and colleagues use deep reinforcement learning and a multiplex network representation to avoid the heavy computational cost. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Nature Machine Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2025
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