Influential node detection in multilayer networks via fuzzy weighted information

Abstract

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.