Bibliometric Analysis by Network Models

This book describes a new holistic methodology for analyzing a field of scientific literature using a combination of network and semantic analysis of bibliometric data, in order to identify the main citation patterns and most impactful research in a scientific field. It introduces new centrality indices that take into account more complex parameters of nodes than classical indices, such as group influence and the influence of pivotal vertices on other vertices. Other topics covered by the book include pattern analysis and stability metrics; centrality analysis of article citation networks; journal citation networks; and methods of semantic analysis to analyze trends. The book also shows how to analyze the co-occurrence of terms and to learn more about research trends by dividing articles into topical groups. While the scientific literature of Parkinson’s disease (PD) forms the basis of this book, its contents will be useful to researchers in any scientific domain, as well as journal editorial teams, scientific organizations, and investors.