Sean (Xiang) Ren: h-index, Total Citations, and Citation Map
Sean (Xiang) Ren's h-index is 79 (275 i10-index, 31,613+ total citations across 466+ publications) according to Google Scholar as of July 2026. Sean (Xiang) Ren is affiliated with Viterbi Early Career Chair & Associate Professor, University of Southern California.
Sean (Xiang) Ren is a researcher affiliated with Viterbi Early Career Chair & Associate Professor, University of Southern California, specializing in various fields. Their work has been cited 31,613 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Sean (Xiang) Ren's Citation Metrics
Bibliometric impact based on 466 indexed publications.
- H-Index
- 79
- i10-Index
- 275
- Total Citations
- 31,613
- Citing Countries
- 51
As of July 2026.
Sean (Xiang) Ren has an h-index of 79 and 31,613 total citations across 466 publications, with research cited by institutions in 51 countries.
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Global Impact Map
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
20232,813
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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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.
28 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a hierarchical graph representation learning framework using differentiable pooling, establishing a foundational method for scalable graph neural networks.
The researcher developed a framework for quantifying and extrapolating language model capabilities beyond standard imitation benchmarks, establishing a new standard for evaluating AI performance.
The researcher developed GraphRNN, a deep auto-regressive model for generating realistic graphs, establishing a foundational approach in graph generation.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Sean (Xiang) Ren's research
Sean (Xiang) Ren is a researcher at Viterbi Early Career Chair & Associate Professor, University of Southern California. Their work has been cited 31,613 times across 466 publications (h-index 79), according to Google Scholar.
Their most-cited work, “Beyond the imitation game: Quantifying and extrapolating the capabilities of language models” (2023), has accumulated 2,813 citations. Other influential works include “Hierarchical Graph Representation Learning with Differentiable Pooling” (2018) with 2,550 citations and “GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models” (2018) with 1,474 citations.
Citations of Sean (Xiang) Ren's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
