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A Message Passing Neural Network Framework with Learnable PageRank for Author Impact AssessmentSONG, G.   , FU, D. , WU, X. |
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Author keywords
bibliometrics, data mining, neural networks, network topology, semisupervised learning
References keywords
rank(12), networks(7), science(5), ranking(5), personalized(5), information(5), citation(5), index(4), algorithm(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2025-02-28
Volume 25, Issue 1, Year 2025, On page(s): 11 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2025.01002
SCOPUS ID: 86000344468
Abstract
The assessment of author influence is crucial for the advancement of scientific research and policy shaping in academia. PageRank and its derivatives, primarily focusing on network topology, often overlook spatial attributes and exhibit biases, besides being inefficient due to their iterative nature. We propose a novel Neuro-Enhanced PageRank Network (NPRNet), which integrates graph neural networks with PageRank to address these deficiencies. NPRNet utilizes Message Passing Neural Networks to efficiently compute and incorporate learnable parameters, thus considering node attributes. A semi-supervised learning strategy is also developed to manage the absence of true labels. Validated using conference articles in the field of artificial intelligence (AI) from Scopus API since 1985, NPRNet not only enhances computational efficiency but also effectively captures both topological and spatial feature information. It identifies leading countries in AI research, closely aligning with global trends in AI innovation and demonstrating the capacity to recognize recently active authors. This highlights its ability to reflect current research dynamics, thus deepening evaluations by integrating node attributes and supporting advanced knowledge management in research. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.