The science of science and a multilayer network approach to scientists’ ranking

Abstract

The deluge of data on scholarly output created unique opportunities for identifying the drivers of modern science, for studying career paths of scientists, and for measuring the research performance. These massive data and processing methodologies have given rise to an exciting new field, namely Science of Science (SoS) as the successor of what is called scientometrics or informetrics for many decades. Science of Science is the offspring of the fertile cooperation of many disciplines, such as network science, statistics, machine learning, mathematical analysis, sociology of science and so on. In this article, we provide a comprehensive coverage of recent advances in SoS related to network analysis, prediction and ranking, and investigate the issue of scientist ranking from a multilayer network perspective. Towards this goal, we contrast by experiments the well-known h-index and the recently proposed indicator C3-index to a generalization of PageRank for multilayer networks, namely BiPlex PageRank, which is based on solid tensor analysis. Both the obtained results and the brief survey of SoS will deepen our faith to SoS and stimulate further efforts in this transdisciplinary field. © 2018 Association for Computing Machinery.