Premium
Author classification using transfer learning and predicting stars in co‐author networks
Author(s) -
Abbasi Rashid,
Kashif Bashir Ali,
Chen Jianwen,
Mateen Abdul,
Piran Jalil,
Amin Farhan,
Luo Bin
Publication year - 2021
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2884
Subject(s) - computer science , stars , preprocessor , transfer of learning , task (project management) , artificial intelligence , node (physics) , domain (mathematical analysis) , baseline (sea) , machine learning , key (lock) , quality (philosophy) , star (game theory) , data pre processing , deep learning , data mining , mathematics , engineering , mathematical analysis , philosophy , oceanography , computer security , systems engineering , structural engineering , epistemology , computer vision , geology
Summary 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.