Zihan Wang: h-index, Total Citations, and Citation Map
Zihan Wang's h-index is 6 (4 i10-index, 771+ total citations across 16+ publications) according to Google Scholar as of May 2026. Zihan Wang is affiliated with Northeastern University (China).
Zihan Wang is a researcher affiliated with Northeastern University (China), specializing in Recommendation System. Their work has been cited 771 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Zihan Wang's Citation Metrics
Bibliometric impact based on 16 indexed publications. Of these, 14 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 6
- i10-Index
- 4
- Total Citations
- 771
- Citing Countries
- 33
As of May 2026.
Zihan Wang has an h-index of 6 and 771 total citations across 16 publications, with research cited by institutions in 33 countries.
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Global Impact Map
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Top Cited Works
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Is mamba effective for time series forecasting?
2025376
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
335 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered deep search agents using knowledge graphs and multi-turn RL, establishing a framework subsequently adapted for specialized medical AI applications.
The researcher critically evaluated the effectiveness of Mamba architectures for time series forecasting, establishing a foundational benchmark that has garnered significant independent scholarly attention.
The researcher developed token and sequence-level reward shaping methods incorporating policy entropy to enhance reinforcement learning training stability and efficiency.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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