## 2 Model and Method  
In this part, we first use the dynamic network model to show how the topological structures of DSCNs are constantly evolving. Using this model as a guide, we look at people's social connections and movement patterns to create new interpretations of traditional influence measurements that are based on the network's dynamic.  
A graph \(G\) is made up of a limited number of nodes (\(V\)) and edges (\(E\)). Since there cannot be an empty set of nodes, \(V\
eq\varnothing\), and thus \(V=\{v_{1},v_{2},\ldots,v_{n}\}\). Pairs of nodes \(\big{(}v_{i},v_{j}\big{)}\) that represent some sort of connection pattern between nodes make up the collection of edges \(E\). The terms nearby and neighboring are used to describe two nodes connected by an edge. The network is referred to as undirected if the edges are unordered, where \(\big{(}v_{i},v_{j}\big{)}=\big{(}v_{j},v_{i}\big{)}\).  
If there is a relationship between the nodes \(v_{i},v_{j}\), then an adjacency matrix \(M\) with elements \(m_{ij}=1\) and \(0\) otherwise can be used to fully describe network \(G\). Unweighted or binary networks are examples of this. In general, \(G\) is characterized using an adjacency matrix, where \(m_{ij}\geq 1\) if there is an edge between nodes \(v_{i},v_{j}\), and \(0\) otherwise, where the edges contain a numerical value measuring a feature of the edge. A network \(G\) is also considered to be connected if, for every pair of distinct nodes \(v_{i},v_{j}\), there is a route from \(v_{i}\) to \(v_{j}\); otherwise, it is said to be unconnected [25]. However, complex networks are a special kind of graph in which the nodes and edges have complicated organizational structures and non-trivial topological properties. Since these networks contain complicated patterns and features, the interactions between the pieces in this situation are not clear-cut or simple.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Objective** & **Methodology** & **Key Findings** \\ \hline \multirow{3}{*}{[20]} & Detection of influential nodes in & Time-ordered weighted graph & \multirow{3}{*}{New hybrid centrality measure: Temporal Closeness-Closeness measure.} \\  & dynamic weighted networks. & & \\  & & Integrate degree, constraint & \\  & Identification of & coefficient, and k-shell for a & Centripetal centrality as an effective \\  & influential spreaders. & comprehensive assessment of & measure to identify influential nodes. \\  & & node importance. & \\  & Prediction of the dynamics and & & \\  & evolution of a & Two-layer HMM to model & \multirow{3}{*}{MONDE, demonstrating prediction accuracy rates for dynamics and evolution in social networks.} \\  & social network. & & \\  & Detection of critical nodes of & Compare centrality & Isolating centrality as an effective \\  & networks. & measures’ effectiveness. & measure for identifying critical nodes. \\  & Correlation between seed node & Investigate different centrality & Emphasize the impact of network \\  & detection and information flow. & measures for seed node detection. & structure on seed node selection. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related works on the detection of the most influential nodes in a network.