  Scholarly articles publishing and getting cited has become a way of life for
academicians. These scholarly publications shape up the career growth of not
only the authors but also of the country, continent and the technological
domains. Author affiliations, country and other information of an author
coupled with data analytics can provide useful and insightful results. However,
massive and complete data is required to perform this research. Google scholar
which is a comprehensive and free repository of scholarly articles has been
used as a data source for this purpose. Data scraped from Google scholar when
stored as a graph and visualized in the form of nodes and relationships, can
offer discerning and concealed information. Such as, evident domain shift of an
author, various research domains spread for an author, prediction of emerging
domain and sub domains, detection of journal and author level citation cartel
behaviors etc. The data from graph database is also used in computation of
scholastic indicators for the journals. Eventually, econometric model, named
Cobb Douglas model is used to compute the journals Modeling "Internationality"
Index based on these scholastic indicators.
