Xiaojun Chang Google Scholar: h-index, Total Citations, and Citation Map
Xiaojun Chang's h-index is 81 (283 i10-index, 29,988+ total citations across 496+ publications) according to Google Scholar as of September 2026. Xiaojun Chang is affiliated with Chair Professor in Artificial Intelligence, University of Science and Technology of China.
Xiaojun Chang is a researcher affiliated with Chair Professor in Artificial Intelligence, University of Science and Technology of China, specializing in various fields. Their work has been cited 29,988 times. This profile visualizes their global influence, spanning a global audience.
Xiaojun Chang's Citation Metrics
Bibliometric impact based on 496 indexed publications. Of these, 17 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 81
- i10-Index
- 283
- Total Citations
- 29,988
- Citing Countries
- 0
As of September 2026.
Xiaojun Chang has an h-index of 81 and 29,988 total citations across 496 publications, with research cited by institutions in 0 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Xiaojun Chang's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Connecting the dots: Multivariate time series forecasting with graph neural networks
20203,213
Top Citing Countries
Top Citing Institutions
No institution data available.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Xiaojun Chang's research
Xiaojun Chang is a researcher at Chair Professor in Artificial Intelligence, University of Science and Technology of China. Their work has been cited 29,988 times across 496 publications (h-index 81), according to Google Scholar.
Their most-cited work, “Connecting the dots: Multivariate time series forecasting with graph neural networks” (2020), has accumulated 3,213 citations. Other influential works include “A survey of deep active learning” (2021) with 2,261 citations and “A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions” (2021) with 1,239 citations.
