  The world's collective knowledge is evolving through research and new
scientific discoveries. It is becoming increasingly difficult to objectively
rank the impact research institutes have on global advancements. However, since
the funding, governmental support, staff and students quality all mirror the
projected quality of the institution, it becomes essential to measure the
affiliation's rating in a transparent and widely accepted way. We propose and
investigate several methods to rank affiliations based on the number of their
accepted papers at future academic conferences. We carry out our investigation
using publicly available datasets such as the Microsoft Academic Graph, a
heterogeneous graph which contains various information about academic papers.
We analyze several models, starting with a simple probabilities-based method
and then gradually expand our training dataset, engineer many more features and
use mixed models and gradient boosted decision trees models to improve our
predictions.
