Upload Records Snowball Search Search OpenAlex
About the database ScholarIQanswers from OpenAlex & ORCID
How has Xing Tang's publication output changed over time?
ScholarIQpublication output · 2017–2026
Output grew0% over the shown period — from 1 works in 2017 to 1 in 2026.
1
1
4
1
2
1
1
2017201920212022202320242026
What are the most-cited papers on Xing Tang?
ScholarIQmost cited works
Knowledge representation learning with entity descriptions, hierarchical types, and textual relations
Xing Tang, Ling Chen, Jun Cui, Baogang Wei
Information Processing & Management. 201966 Citations
RLPath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning
Ling Chen, Jun Cui, Xing Tang, Yuntao Qian, Yansheng Li, Yongjun Zhang
S74726891. 202143 Citations
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
Rong Hu, Ling Chen, Shenghuan Miao, Xing Tang
S4210191458. 202340 CitationsOPEN ACCESS
The influence of extracurricular activities on middle school students’ science learning in China
Danhui Zhang, Xing Tang
S54332258. 201735 Citations
DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical Relation
Ling Chen, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, Yongjun Zhang
S41523882. 202124 Citations
Related on ScholarIQ
Shenyang Pharmaceutical University
Institution
Knowledge representation learning with entity descriptions, hierarchical types, and textual relations
Paper
RLPath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning
Paper
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
Paper
The influence of extracurricular activities on middle school students’ science learning in China
Paper
DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical Relation
Paper