Finding Influential Institutions in Bibliographic Information Networks

TL;DR

This paper addresses the problem of ranking research institutions in bibliographic information networks, proposing solutions for the KDD Cup 2016 competition and demonstrating effective ranking performance.

Contribution

It introduces novel methods for ranking research institutions in bibliographic networks, extending beyond traditional paper and author ranking approaches.

Findings

Achieved an average NDCG@20 score of 0.7483

Ranked 11th in the KDD Cup 2016 competition

Provided solutions for multi-phase ranking challenges

Abstract

Ranking in bibliographic information networks is a widely studied problem due to its many applications such as advertisement industry, funding, search engines, etc. Most of the existing works on ranking in bibliographic information network are based on ranking of research papers and their authors. But the bibliographic information network can be used for solving other important problems as well. The KDD Cup $2016$ competition considers one such problem, which is to measure the impact of research institutions, i.e. to perform ranking of research institutions. The competition took place in three phases. In this paper, we discuss our solutions for ranking institutions in each phase. We participated under team name "anu@TASL" and our solutions achieved the average NDCG@$20$ score of $0.7483$, ranking in eleventh place in the contest.