Jure Leskovec Google Scholar: h-index, Total Citations, and Citation Map
Jure Leskovec's h-index is 182 (455 i10-index, 269,786+ total citations across 696+ publications) according to Google Scholar as of November 2026. Jure Leskovec is affiliated with Stanford University.
Jure Leskovec is a researcher affiliated with Stanford University, specializing in Machine Learning, AI, Graph Neural Networks. Their work has been cited 269,786 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jure Leskovec's Citation Metrics
Bibliometric impact based on 696 indexed publications.
- H-Index
- 182
- i10-Index
- 455
- Total Citations
- 269,786
- Citing Countries
- 105
As of November 2026.
Jure Leskovec has an h-index of 182 and 269,786 total citations across 696 publications, with research cited by institutions in 105 countries.
Global Impact Map
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Inductive Representation Learning on Large Graphs
201726,097
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.
4965 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established scalable feature learning for networks via node2vec, a foundational approach that enabled subsequent graph-based machine learning applications.
The researcher established foundational frameworks for analyzing user mobility in social networks and extending graph-based learning to explainability and clinical applications.
The researcher established foundational methodologies for mining massive datasets, extending this work to address temporal dynamics in interaction networks and graph machine learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Jure Leskovec's research
Jure Leskovec is a researcher in Machine Learning, AI and Graph Neural Networks at Stanford University. Their work has been cited 269,786 times across 696 publications (h-index 182), according to Google Scholar.
Their most-cited work, “Inductive Representation Learning on Large Graphs” (2017), has accumulated 26,097 citations. Other influential works include “node2vec: Scalable feature learning for networks” (2016) with 16,877 citations and “How powerful are graph neural networks?” (2018) with 15,315 citations.
Citations of Jure Leskovec's research come primarily from United States, China 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.
