X Ren Google Scholar: h-index, Total Citations, and Citation Map
X Ren's h-index is 79 (275 i10-index, 31,613+ total citations across 465+ publications) according to Google Scholar as of October 2026. X Ren is affiliated with University of Southern California.
X Ren is a researcher affiliated with 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.
X Ren's Citation Metrics
Bibliometric impact based on 465 indexed publications.
- H-Index
- 79
- i10-Index
- 275
- Total Citations
- 31,613
- Citing Countries
- 78
As of October 2026.
X Ren has an h-index of 79 and 31,613 total citations across 465 publications, with research cited by institutions in 78 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
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.
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 X Ren's research
X Ren is a researcher at University of Southern California. Their work has been cited 31,613 times across 465 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 X 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.
