Predicting the future relevance of research institutions - The winning solution of the KDD Cup 2016
This addresses the need for transparent and widely accepted metrics to evaluate research institutions for stakeholders like funders and policymakers, though it is incremental in applying existing machine learning methods to this specific domain.
The authors tackled the problem of objectively ranking research institutions' future impact by predicting their number of accepted papers at future academic conferences, achieving a winning solution in the KDD Cup 2016 competition.
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.