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Author classification using transfer learning and predicting stars in co-author networks

Abbasi, R and Kashif Bashir, A and Jianwen, C and Mateen, A and Piran, J and Amin, F and Luo, B (2020) Author classification using transfer learning and predicting stars in co-author networks. Software - Practice and Experience. ISSN 0038-0644

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Abstract

© 2020 John Wiley & Sons Ltd The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

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