## 3 Proposed Methodology  
### Background of Network Centrality Measures  
The concept of network centrality was first discussed in [33]. In a network, identifying the most important nodes is a very important task in order to efficiently control the flow ofinformation. In a graphical network, a few nodes are positioned in the network in such a way that they define communication paths for the nodes at the periphery of the network. Due to their optimal structural position in the network, their reachability to different parts of the network and their accessibility from other nodes are high. As can be seen in Figure 3, the central node has the highest centrality value due to its better accessibility to other nodes (in terms of path length).  
However, the notion of importance for the nodes in the network depends on the choice of the task to be accomplished. In some cases, the node with the most connections in the network is important, while in other cases, the node through which most edge connections pass is more important. Based on these criteria, there are different types of network centrality measures.  
When measuring degree centrality, nodes with more links in the network [34] are given higher importance. [PERSON] [35] was the first to introduce closeness centrality. Degree centrality may not be a good criterion for defining the importance of a node in the network. Closeness centrality defines the importance of a node based on the notion of closeness to other nodes. However, this was later redefined as harmonic centrality [36] because the functional definition for closeness centrality is not valid for an unconnected network. The computation of Eigenvector centrality for a node is based on what kind of nodes the underlying node is connected to [37].  
The betweenness centrality metric [38] is a path-based measure based on two conjectures: node betweenness and edge betweenness. The latter, however, depends on the node betweenness. Here, the importance metric of the node depends on how often it occurs on the shortest paths from one node to another. We then can obtain Equation (11) as:  
\[c_{bet}(x)=\sum_{y\
eq\
eq x,\sigma_{yz}\
eq 0}\frac{\sigma_{yz}(x)}{\sigma_{ yz}} \tag{11}\]  
Here, \(\sigma_{yz}\) stands for the number of shortest paths from \(y\) to \(z\) and \(\sigma_{yz}(x)\) for the number of these paths passing through x. The centrality measure has several implications for finding the important nodes in the network when the flow of information between two groups is mediated by a node or group of nodes. [PERSON] [39] proposed an efficient algorithm for computing betweenness centrality. The time complexity of [PERSON] algorithm is \(\mathcal{O}(nm+n^{2}logn)\), and the space complexity is \(\mathcal{O}(n+m)\), where \(n\) and \(m\) are the number of nodes and edges in a graph, respectively.  
These are some basic centrality measures to identify the important nodes of the network based on their structural positions. Current research further explores the applicability and usability of centrality measures depending on the network structures and applications. In this proposed work, we use this concept to identify a set of nodes for training the Graph Neural Network. To improve the training dataset, we choose nodes with high betweenness centrality values. In the next subsection, we justify this statement with a detailed analytical reasoning.