## 1 Introduction
However, it is worth noting that the effectiveness of this proposed measure is influenced by the structure of the nodes' neighborhood, especially in detecting critical nodes that segregate the network into connected components. This dependency might limit its effectiveness in certain low-density topologies. The authors of [24] focus on seed node selection in online social networks (OSNs) for information propagation and influence maximization. The study explores various centrality measures, such as clustering coefficients and node degree, to identify influential seed nodes. It considers Twitter as a platform for opinion generation and discusses the relevance of centrality measures as seed nodes in large-scale networks. The study also conducts a comparative analysis using benchmark similarity measures to assess the effectiveness of different centrality measures in seed node selection. The study acknowledges that the effectiveness of seed node selection is influenced by the network's structure. Certain propagation approaches, like Random Walk, are affected by local clustering. This sensitivity to network structure implies that the effectiveness of the proposed approach could vary significantly in network topologies with insufficient connections. In summary, these works propose or utilize specific centrality measures to assess the importance of nodes in a network, hence their focus on identifying influential or critical nodes within the network as shown in Table 1. These nodes are deemed essential for information propagation and can play a crucial role in maximizing information coverage within the network. However, the objective is not always to maximize the pathways through which information circulates. In communication networks, it is preferable to maintain low overhead values to avoid unnecessary consumption of memory resources in intermediary devices forwarding data to their destinations. Furthermore, these works exhibit a certain dependence on network topology, implying that effectiveness could vary in cases of low connection density, as observed in opportunistic networks. Based on the aforementioned concerns, this research paper aims to identify and rank hub nodes using a dynamic network model to analyze how device connections evolve over time. For that, the behavior of a DSCN is gathered by a progression of graphs as the devices connect and disconnect throughout the network operation. First, we introduce a novel local centrality metric, Dynamic Degree centrality, as we believe that both the number of neighbors and the frequency of connections with them serve as valuable cues of a node's importance in the network. This metric seamlessly integrates both factors, effectively gauging the node's centrality based on the progression of its connections and contact frequency with neighboring nodes. Furthermore, we have developed a closeness centrality measure to address the potential impact of longer forwarding delays on storage capacity utilization (network overhead) and its subsequent influence on data forwarding likelihood. To quantify a device's global centrality, we propose the Dynamic Closeness centrality based on the temporal evolution network model, which considers forwarding overhead. We also propose the Social-based Closeness Centrality Metric, which considers social relations to provide an effective centrality metric to ensure that data are carried and forwarded by relay devices with a high likelihood of reaching the destination host. This is because social relations and behaviors among wireless users are typically long-term characteristics and less fluctuating than device mobility. Thus, to assess the usefulness of our suggested centrality metrics and to examine the properties of the centrality distribution applied to various Quality of Service (QoS) measurements, we evaluate the results of experiments run on real-world datasets. The remainder of this paper is organized as follows: Section 2 provides an overview of the dynamic network model used.