Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals

Authors:
Alfonso Ibáñez;Pedro Larrañaga;Concha Bielza
Affiliations:
Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain 28660;Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain 28660;Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain 28660
Venue:
Scientometrics
Year:
2011

Citing 8
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks

Artificial Intelligence
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Expert Systems and Probabiistic Network Models

Expert Systems and Probabiistic Network Models
Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine

Journal of the American Society for Information Science and Technology
An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index

Journal of the American Society for Information Science and Technology
Eigenfactor: Does the principle of repeated improvement result in better estimates than raw citation counts?

Journal of the American Society for Information Science and Technology
How are new citation-based journal indicators adding to the bibliometric toolbox?

Journal of the American Society for Information Science and Technology

Cluster methods for assessing research performance: exploring Spanish computer science

Scientometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

As they are used to evaluate the importance of research at different levels by funding agencies and promotion committees, bibliometric indices have received a lot of attention from the scientific community over the last few years. Many bibliometric indices have been developed in order to take into account aspects not previously covered. The result is that, nowadays, the scientific community faces the challenge of selecting which of this pool of indices meets the required quality standards. In view of the vast number of bibliometric indices, it is necessary to analyze how they relate to each other (irrelevant, dependent and so on). Our main purpose is to learn a Bayesian network model from data to analyze the relationships among bibliometric indices. The induced Bayesian network is then used to discover probabilistic conditional (in)dependencies among the indices and, also for probabilistic reasoning. We also run a case study of 14 well-known bibliometric indices on computer science and artificial intelligence journals.