## 2 Methods
### Social Network Analysis Methodology  
The Social Network Analysis (SNA) method is an interdisciplinary research technique that focuses on analyzing the structure, dynamics, and functioning of social networks by examining the relationships between individuals within these networks [26]. SNA facilitates the analysis of social relationships and structures using networks and graph theory [31; 32]. The application of the SNA method can be divided into five main steps. The first step is to define the purpose of the investigation, understand the network structure being analyzed by mapping connection patterns, identifying key nodes or connections, analyzing flows within the network, and identifying clusters or modules [32]. The second step involves collecting and preparing data. Depending on the case study, the data may originate from existing datasets, surveys, interviews, or expert research. After collecting the data, the next step is to analyze them [33]. Various types of IT tools are commonly used for analysis, including Python with NetworkX, the R programming language with the SNA package, or Gephi for network analysis and visualization [34; 35; 36; 37]. Analyses are performed on network measures, which include the following [34; 35; 36; 37]:  
* Degree--The number of edges connected to a given vertex. In directed networks, we distinguish between the input degree (number of input edges) and the output degree (number of output edges). * Centrality Degree \(C_{D}(v)\)--This measures the number of direct connections (edges) a node has with other nodes in the network. In directed networks, a distinction can be made between input and output degree centrality. The degree centrality highlights the activity of a node in the network through the number of its direct connections. It is expressed by Formula (1): \[C_{D}(v)=\frac{\deg(v)}{N-1}\] (1) where \(\deg(v)\)--Node degree; N--Number of vertices. * Betweenness Centrality \(C_{-B}(v)\)--This measures how often a node appears on the shortest paths between pairs of other nodes in the network. A high value of betweenness centrality indicates that the node act as a \"bridge\" or intermediary in the flow of information or resources in the network, which may indicate control over what flows in the network and how. It is expressed by Formula (2): \[C_{B}(v)=\sum_{s,t\;ev}\frac{\sigma_{st}(v)}{\sigma_{st}}\] (2) where \(\sigma_{st}\) represents the total number of shortest paths between vertices \(s\) and \(t\), while \(\sigma_{st}(v)\) denotes the number of those paths that pass through vertex \(v\). * Closeness Centrality \(C_{-C}(v)\)--This quantifies how near a node is to all other nodes in the network, considering the shortest path distances. It is calculated as the reciprocal of the total length of the shortest paths from the given node to every other node. A high closeness centrality suggests that a node can reach others in the network, which may be beneficial for the speed of information flow. It is expressed by Formula (3): \[C_{C}(v)=\frac{1}{\sum_{s,t\;ev}d(v,t)}\] (3)where \(d(v,t)\)--Length of the shortest path from \(v\) to \(t\). * Density--A measure that determines how close a network is to a fully connected network (where every node is connected to every other node). The network density provides the proportion of the number of actual connections (edges) in the network to the number of possible connections. It is determined by Formula (4): \[D=\frac{2L}{N(N-1)}\] (4) where \(L\)--Number of edges. * Average Path Length--A measure that determines how close, on average, each node is to every other node in the network. It indicates the average number of steps (edges) needed to move from one node to another if the journey always takes the shortest possible path. It is determined by Formula (5): \[l_{g}=\frac{1}{N(N-1)}\sum_{i\
eq j}\,d\big{(}v_{i},\,v_{j}\big{)}\] (5) The penultimate step involves interpreting the results obtained.