# Leveraging Social Network Analysis for Crowdsourced Software Engineering Research
## 2 Literature Review  
The use of social networks in scientific research has increased because of the growing interest in understanding the determinants of scientific production and to gain insights into collaborative patterns [13]. In this section, a literature review describes representative studies on the use of social networks in understanding scientific research in general and software engineering research in particular. For this purpose, we selected studies representing the application of SNA for visualizing and analyzing research in different fields, such as biology, tourism and hospitality, management and accounting, and software engineering, and for providing different types of research contributions, such as co-authorship networks and citation networks. One study [14] analyzed the co-authorship network of parasitologists in terms of their research performance. It showed that the number of publications in the field of parasitology was high and increasing and that the network had a medium density, meaning that authors collaborated with each other to a moderate extent. Centrality metrics of SNA were used to measure authors' centrality and the importance of author positioning within a network. [PERSON] et al. [7] used SNA to analyze research collaborative patterns among UTM (i.e., university) researchers. SNA measures were used to find the most influential and reliable researchers. They found that some researchers preferred to work with their colleagues in the same department rather than other departments, and this resulted in fewer interdepartmental collaborations. Therefore, they suggested that researchers increase the rate of collaboration with other departments to improve their publication productivity and performance. SNA offered important insights on collaborative patterns at both the individual and institutional levels that could motivate the growth of academic disciplines, institutions, and regions [7]. [PERSON] et al. [15] used SNA to analyze the six top tourism and hospitality journals to determine their cross-institutional collaborative networks. They concluded that there were important relationships between research performance and a university's centricity position in networks. [PERSON] et al. [8] used SNA to analyze biomedical research collaborative networks and gained an understanding of the generative techniques of research collaboration and identified collaborations that would be beneficial to researchers in the biomedical research community. [PERSON] et al. [16] examined the social network of co-authorship publications in management and accounting sciences in Brazil to determine how researchers' importance was related to their research experience, gender, and geography. They found that male authors dominated prominent positions of the collaboration network. They occupied central positions more than women based on the number of direct links and collaborations. The research experience of authors was important in terms of establishing collaboration links, and academic degrees had a positive relationship with strong network centrality. Regarding geography, the author's location was not important in terms of occupying a central position. SNA has also been used in new research areas to determine the ways in which research collaboration advances the main research goals and to identify the leading groups in this new research area. For instance, [PERSON] et al. [17] used SNA to analyze the co-authorship network of those researching the Zika virus. Their goal was to understand how scientists collaborated on research about this new disease and identify the leading Zika research groups and most influential researchers. The study revealed that the number of publications, diversified types of collaboration, and links established with the research area's pioneers determined the influence of the researcher. The other common model of using SNA is to understand citation networks.