Author classification using transfer learning and predicting stars in co‐author networks. (28th September 2020)

• Record Type: Journal Article
• Authors: Abbasi, Rashid; Kashif Bashir, Ali; Chen, Jianwen; Mateen, Abdul; Piran, Jalil; Amin, Farhan; Luo, Bin
• Other Names: Baker Thar guestEditor.; Al‐Jumeily Dhiya guestEditor.; Maamar Zakaria guestEditor.; Tari Zahir guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 3(2021)
• Page Start: 645
• Page End: 669
• Publication Date: 2020-09-28
• Subjects: author classification -- semantic web -- social network -- transfer learning; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2884
• Languages: English
• ISSNs: 0038-0644

Abstract

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.