  Scientific collaboration networks are an important component of scientific
output and contribute significantly to expanding our knowledge and to the
economy and gross domestic product of nations. Here we examine a dataset from
the Mendeley scientific collaboration network. We analyze this data using a
combination of machine learning techniques and dynamical models. We find
interesting clusters of countries with different characteristics of
collaboration. Some of these clusters are dominated by developed countries that
have higher number of self connections compared with connections to other
countries. Another cluster is dominated by impoverished nations that have
mostly connections and collaborations with other countries but fewer self
connections. We also propose a complex systems dynamical model that explains
these characteristics. Our model explains how the scientific collaboration
networks of impoverished and developing nations change over time. We also find
interesting patterns in the behaviour of countries that may reflect past
foreign policies and contemporary geopolitics. Our model and analysis gives
insights and guidelines into how scientific development of developing countries
can be guided. This is intimately related to fostering economic development of
impoverished nations and creating a richer and more prosperous society.
