## 1 Introduction  
The vast increase in autonomous and heterogeneous wireless devices poses challenges for the future of communication systems due to the complexity of their interconnection, which involves multiple networking technologies and a wide range of device capabilities. According to statistics provided by Statista [1], the global number of smartphones reached nearly 6.6 billion in 2022 and is projected to surpass 7.8 billion by 2028. In other words, a world of pervasive mobile devices is being built that has vast processing capabilities and allows for smooth communication among them, enabling greater connectedness. Each node or gadget in a mobile communication network is a connecting point that is innately connected to a person who is moving and takes part in the network's data exchange. Moreover, Mobile Social Networks (MSNs) leverage wireless devices and function as a communication infrastructure designed for point-to-point and short-range communications, seeking increased data exchange efficiency by adapting to the typical movements and behaviors of individuals using mobile devices [2, 3, 4, 5]. In this regard, opportunistic networks emerge as a classification of wireless networks characterized by sporadic, unreliable, or constrained user-to-user ad hoc connections [6]. In such networks, conventional routing algorithms often depend on the \"storage-carry-and-forward\" approach, whereby a node forwards messages to a varying number of neighboring nodes it encounters based on the specific routing algorithm employed. Nevertheless, this flooding strategy can result in a proliferation of message duplicates, potentially leading to network and device congestion [7]. Within such a context, a Dynamic Social Complex Network (DSCN) can be defined as a network that leverages human social behavior, such as daily routines, mobility patterns, and interests, to facilitate message routing and data sharing over time. In these networks, nodes (users with mobile devices) can form on-the-fly social networks to communicate with each other. Considering users' social routines when determining whether a node should retransmit a message to another node can reduce transmission delay and routing overhead [8]. Consequently, minimizing routing overhead will decrease the average number of hops that message routes traverse before reaching their destination. The search for the most influential or important nodes is a critical component of analyzing and comprehending the network topology dynamics due to its intrinsic role in determining the network's overall structure or efficiency [9; 10; 11]. Influential devices often act as key hubs for data exchange and efficient communication pathways and, depending on the objective, the significance of a node can vary. There are several metrics of node importance, such as degree centrality, closeness centrality, or social centrality. The first refers to the number of its direct connections to other nodes and analyzes its level of activity in the network's topology compared to others. Closeness centrality measures a node's proximity to other nodes and indicates how efficiently it can access or distribute information within the network. Finally, social centrality aims to capture the extent to which certain nodes in the network hold influential positions in terms of data forwarding or control over the network's social dynamics. Social centrality metrics take into account factors such as connectivity, interactions, and relationships among nodes to assess their relative significance in the social behavior of the network. However, conventional centrality metrics used in traditional networks are not useful in DSCNs, as they rely on a static network model where there are multiple connections and disconnections over time that are usually aggregated into a single binary network. As a result, traditional metrics have been extended to work with weighted or dynamic topologies [12; 13; 14; 15].