The Academic Social Network

TL;DR

This paper introduces new ranking metrics for academic authors, venues, and institutions based on influence, connections, and exposure, going beyond traditional citation metrics, and discusses their computation and implications.

Contribution

The paper proposes novel metrics for evaluating academic impact and relationships, and explores their computation and application to institution rankings.

Findings

New influence, connections, and exposure metrics for authors.

Metrics can be computed efficiently and relate to traditional measures.

Institution rankings derived from author metrics are feasible.

Abstract

Through academic publications, the authors of these publications form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors pick co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Libra and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to find out, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities -authors, publication venues and institutions. We go beyond traditional metrics such as paper counts, citations and h-index. Specifically, we define metrics such as influence, connections and exposure for authors. An author gains influence by receiving more citations, but also citations from influential authors. An author increases his/her connections by co-authoring with other authors, and specially from other authors with high connections. An author receives exposure by publishing in selective venues where publications received high citations in the past, and the selectivity of these venues also depends on the influence of the authors who publish there. We discuss the computation aspects of these metrics, and similarity between different metrics. With additional information of author-institution relationships, we are able to study institution rankings based on the corresponding authors' rankings for each type of metric as well as different domains. We are prepared to demonstrate these ideas with a web site (http://pubstat.org) built from millions of publications and authors.