Author classification using transfer learning and predicting stars in co-author networks
- Authors
- Abbasi, Rashid; Kashif Bashir, Ali; Chen, Jianwen; Mateen, Abdul; Piran, Jalil; Amin, Farhan; Luo, Bin
- Issue Date
- Mar-2021
- Publisher
- WILEY
- Keywords
- author classification; semantic web; social network; transfer learning
- Citation
- SOFTWARE-PRACTICE & EXPERIENCE, v.51, no.3, pp.645 - 669
- Journal Title
- SOFTWARE-PRACTICE & EXPERIENCE
- Volume
- 51
- Number
- 3
- Start Page
- 645
- End Page
- 669
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80761
- DOI
- 10.1002/spe.2884
- ISSN
- 0038-0644
- Abstract
- 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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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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