## 3 The Results
### Results of the Literature Review
In summary, identifying clusters involves building a citation network, identifying thematic groups, labeling them, and evaluating their internal coherence. This process provides a structured and in-depth view of the dynamics within a research field, facilitating the identification of emerging trends and key work. In this research, 181 groups were identified, representing groups of scientific documents that share a high degree of similarity in terms of citations, keywords, or other attributes. Displaying too many clusters could make the map too complex and difficult to interpret, so only those that significantly contribute to understanding research trends or citation dynamics are represented. In this case study, there are 10 identified thematic clusters. Thematic clusters, represented in Figures 3 and 4, are those with high silhouette scores and internal coherence because they are easier to interpret and represent groups of articles with well-defined themes. In Figure 3, the cluster view is represented, showing a hybrid network of increasing citation terms and highly cited references. Nodes represent individual publications within the clusters, and links represent the connections between them. The labels and annual citations of each cited reference are turned off in this image, as it aims to represent the most significant clusters, each in a different color. The most significant groups are represented, considering those obtained using the LLR algorithm, as it is preferable for its ability to identify unique and meaningful labels from a thematic perspective [17]. Significant clusters are those with the most publications. Clusters are numbered in the descending order of the cluster size, starting from the largest cluster #0. The cluster label reflects the main content of the publications it encompasses. In Figure 4, a temporal representation of the most significant clusters is plotted; it describes a profile of each cluster over time. For each cluster, the evolution of publications over time can be observed, and the peaks represent the highly active study within the given cluster. Table 3 shows the major identified groups along with their silhouette values, the number of studies, and the average year. The _silhouette_ column shows the homogeneity  
Figure 4: Landscape thematic clusters identified. Figure 3: Thematic clusters identified. of a cluster. The higher the silhouette score, the more consistent the cluster members are, provided that the clusters in comparison have similar sizes. If the cluster size is small, then high homogeneity does not mean much. The average year of publication of a cluster indicates whether it is formed by generally recent papers or old papers. The global landscape of V2G research is characterized by contributions from numerous journals and academic institutions from all over the world, reflecting the international nature of this topic. In Figure 5, the countries with the highest number of V2G studies and the corresponding amounts are shown. Geographic analysis clearly highlights the level of collaboration between countries and institutions in research. Setting \"Country\" as the type of nodes, a network of nodes and edges was generated, as shown in Figure 6. The largest nodes represent countries with a large number of studies, while the different colors of the edges indicate collaboration relationships in various years of publication.