Xiaoyong Jin

1.7k citations
3 papers · 66 indexed · h-index 3
Topics
Advanced Text Analysis Techniques (1 paper)COVID-19 epidemiological studies (1 paper)Natural Language Processing Techniques (1 paper)
Journals
Society for Industrial and Applied Mathematics eBooksarXiv (Cornell University)
Partner nations
United States

In The Last Decade

Xiaoyong Jin

3 papers receiving 60 citations

Peers

Xiaoyong Jin
Comparison fields: 5 of 30
  • Artificial Intelligence 33
  • Signal Processing 25
  • Management Science and Operations Research 24
  • Electrical and Electronic Engineering 14
  • Computer Vision and Pattern Recognition 7
Replace Xiyou Zhou with:
Xiyou Zhou United States
Prashant Vats India
Karan Malik India
Luiz André P. Paes Leme Brazil
Enming Yuan China
Miodrag Lovrić Serbia
Madelon Hulsebos United States
Jan Pfeifer United States
Jacob Montiel New Zealand
Xiaoyong Jin relative to Xiyou Zhou United States Xiyou Zhou's profile →
Citations per field
00.5×1.5×
Xiyou Zhou · 1×
Citations per year

Countries citing papers authored by Xiaoyong Jin

Since Specialization
Citations

This map shows the geographic impact of Xiaoyong Jin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiaoyong Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoyong Jin more than expected).

Fields of papers citing papers by Xiaoyong Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaoyong Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiaoyong Jin. The network helps show where Xiaoyong Jin may publish in the future.

Co-authorship network of co-authors of Xiaoyong Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoyong Jin. A scholar is included among the top collaborators of Xiaoyong Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaoyong Jin. Xiaoyong Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

3 of 3 papers shown
#WorkIndexed citations
1 15
2 4
3
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
47

About Xiaoyong Jin

Xiaoyong Jin is a scholar working on Modeling and Simulation, Artificial Intelligence and Signal Processing, having authored 3 papers that have together received 66 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (1 paper), COVID-19 epidemiological studies (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Signal Processing (25 citations), Health Informatics (3 citations) and Management Science and Operations Research (24 citations). Xiaoyong Jin has collaborated with scholars based in United States. Frequent co-authors include Xiyou Zhou, Xifeng Yan, Yu-Xiang Wang, Yao Xuan, Shiyang Li, Wenhu Chen, Zhiyu Chen and William Yang Wang. Their work appears in journals such as Society for Industrial and Applied Mathematics eBooks and arXiv (Cornell University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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