Scientific Map Analysis and Visualization of Articles Published In the Journal of Research in School and Virtual Learning Using the Social Networks Analysis Approach

Today, with the advancement of communication technologies, particularly the Internet, we are witnessing the generation of a vast amount of information. In academic research, it is crucial to identify the most frequently studied topics and challenges in each field, as well as to determine their significance. One way to evaluate scientific research in any field is by analyzing its scientific map. One of the most effective methods for visualizing and analyzing a scientific map is to utilize network analysis approaches. This technique can effectively demonstrate the structure of scientific networks. 

In this study, we visualized and analyzed the scientific network of articles published in the journal "Research in School and Virtual Learning" using network and co-word analysis methods. A total of 227 articles were included in the analysis. The general research approach includes collecting and cleaning data, constructing matrices of scientific networks, and analyzing and evaluating the results. Various scientific networks of articles, including the co-authorship network, co-organization network, semantic network of articles, and co-occurrence network of words, have been analyzed. PHP language was used for data crawling and processing, while Python language and Gephi software were utilized for network-based analysis and visualization of different networks. In addition, a proposed approach based on the TF-IDF method has been used to calculate the adjacency matrices of each network. This approach includes ten steps. 1) Integrating the title, keywords, and abstract of each article. 

The findings reveal the extent of semantic connection among the published articles in the semantic network. In order to plot and analyze the semantic network of articles, the semantic matrix is obtained by multiplying the word-article matrix with the article-word matrix. The final semantic graph was clustered using the Grivan-Newman algorithm. The top six communities were evaluated based on various metrics. Also, the results showed that there are few articles with high and low degrees, and they are mostly located in the middle of the distribution chart. Therefore, the semantic network of the articles can be classified as a free scale type. The results of the co-occurrence analysis of words show that satisfaction, psychology, and emotion have been addressed more frequently than other topics. The result of the co-authorship network analysis showed that it has a degree of 2.881. Each author has contributed to the writing of the article with three other authors. The average clustering coefficient of the authors in the co-authored network was 0.685. The network exhibited a compactness value of 0.936 and consisted of 83 communities. Additionally, the results showed that the majority of articles were written collaboratively by three authors, followed by four and five authors. The largest component of the co-authorship network of articles was also extracted. Also, the individual network of the top authors in the co-authorship network was drawn and analyzed up to a depth of 3. Personal networks describe a person's relationships in the network with other authors. The structural characteristics of individual networks determine many aspects of a person's cooperative behavior, including the willingness to cooperate and share resources. The analysis of the intra-university collaboration network showed that the researchers of Payam-e-Noor University had the highest number of intra-university collaborations, with 42 articles. After that, Islamic Azad University ranks next with 23 articles. The analysis of the co-university cooperation network showed that each educational institution has participated in the publication of articles with at least three other educational institutions. 

Upon analyzing the word count, it is evident that the topics published in the journal exhibit a wide range of topic distribution. Also, the investigation of the authors' co-authorship network and its clustering showed that the authors of the articles were more inclined to form small scientific groups within their respective organizations, such as universities or research institutes. Also, Payam-e-Noor and Islamic Azad universities have the largest number of co-authored articles within the university. The co-authorship network of Payam-e-Noor University exhibits an interesting and significant structure, indicating a higher willingness to cooperate among university members compared to independent groups. While the authors at Azad University are more inclined to conduct research as independent groups.