Social network analysis and mining intend to explore for certain, previously unknown, and probably useful relational information from social and information networks. In our case, the research paper is about identifying collaborative networks between the authors (co-authors) of Computer Science books with the highlighted focus on the women computer scientist's community. Often the hardest part of collaborating is knowing whom you should be collaborating with. Hence, this study will tackle this issue and will identify, and present a visualization of the co-authors which have already collaborated and how often they have collaborated. In this way, we are going to distinguish the successful collaboration between co-authors, the trend of further collaboration between them and the participation of women on these collaborations. This paper is research which is based on detailed and intensive analysis of the different ways of identifying these kinds of connections through secondary material.
The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles.
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