Ranking Scientific Papers and Venues in Heterogeneous Academic Networks by Mutual Reinforcement

Abstract

Evaluating papers and venues objectively and fairly is a vital and challenging task for scientists, research organizations, and research funding bodies alike. Recently, heterogeneous networks have been used to evaluate papers, authors and venues separately or simultaneously. However, most of the approaches treat all the papers in the citation network equally and ignore the prestige of citing venues and citation time intervals. In this paper, we propose a new framework, MR-Rank, which ranks papers and venues iteratively in a mutually reinforcing way. Several factors including citation time interval, recent performance of a venue, and contribution of a paper to its venue are considered at the same time. Based on the ACL dataset, our experiments show that MR-Rank outperforms other models in terms of ranking effectiveness and efficiency.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.