Mining key nodes in multilayer networks is a topic of considerable importance and widespread interest. This task is crucial for understanding and optimizing complex networks, with far-reaching applications in fields such as social network analysis and biological systems modeling. This paper proposes an effective and efficient fuzzy weighted information model (FWI) to analyze the influential nodes in multilayer networks. In this model, a Joules law model is defined for quantifying the information of the nodes in each layer of the multilayer network. Moreover, the information of the nodes between each layer is then measured by the Jensen–Shannon divergence. The influential nodes in the multilayer network are analyzed using the FWI model to aggregate the information within and between layers. Validation on real-world networks and comparison with other methods demonstrate that FWI is effective and offers better differentiation than existing methods in identifying key nodes in multilayer networks.

中文翻译：

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通过模糊加权信息在多层网络中进行有影响力的节点检测


挖掘多层网络中的关键节点是一个相当重要和广泛关注的话题。这项任务对于理解和优化复杂网络至关重要，在社交网络分析和生物系统建模等领域具有深远的应用。本文提出了一种有效且高效的模糊加权信息模型 （FWI） 来分析多层网络中的影响节点。在这个模型中，定义了一个焦耳定律模型，用于量化多层网络每一层的节点信息。此外，然后通过 Jensen-Shannon 散度测量每层之间的节点信息。使用 FWI 模型分析多层网络中有影响力的节点，以聚合层内和层间的信息。在真实网络上的验证并与其他方法的比较表明，FWI 在识别多层网络中的关键节点方面是有效的，并且比现有方法提供更好的差异化。