Science Explorer Interactive view Map
Company · Hangzhou · China

Alibaba Group (China)

In research, Alibaba Group (China) stands highest in Computer Science (#110 of 4,667 worldwide), Artificial Intelligence (#144 of 1,884 worldwide) and Computer Vision and Pattern Recognition (#84 of 941 worldwide), 2022–2025. Relative to its size it is most specialised in Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence — Computer Vision and Pattern Recognition is 15.4× its share of world research.

World rank, 2022–2025
#2,226
of 28,054 · #4,744 all time
Rank in China
#584
of 3,058
Research works
2.5k
▲ 771% vs 2013–17
Citations
58k
23.2 per fractional work
Top-10% rate
30.1%
record average 16.5%
Open access
32%
world 28%

What is Alibaba Group (China) known for in research?

The fields where it stands highest, 2022–2025, ranked among every institution above the floor in each field.

FieldWorld rankWhere that sitsTop-10% rateWorksAll time
Computer ScienceField #110 of 4,667 30.3%844 #485
Artificial IntelligenceSubfield #144 of 1,884 32.3%295 #319
Computer Vision and Pattern RecognitionSubfield #84 of 941 29.9%241 #235
Physical SciencesDomain #1,423 of 12,888 30.0%1,061 #3201
Information SystemsSubfield #251 of 1,291 34.9%108 #412
EngineeringField #1,656 of 6,636 27.1%166 #3070
Computer Networks and CommunicationsSubfield #246 of 705 24.0%105 #873
Social SciencesDomain #4,443 of 10,521 29.5%115 #7044

Each strip is that field’s whole ranked pool, with the notch where Alibaba Group (China) sits in it, in the colour of the band that rank falls in. The track under the rate is the rate itself: it carries no world mark, because the world rate differs by field (from about 6% to 21% in this record).

Which of those standings moved

The same fields on both windows: where it stood over the whole record, and where it stands in 2022–2025. The distance is the story, and it is the one thing the two rank columns above cannot show.

  1. Computer Science#485 #110
  2. Artificial Intelligence#319 #144
  3. Computer Vision and Pattern Recognition#235 #84
  4. Physical Sciences#3,201 #1,423
  5. Information Systems#412 #251
  6. Engineering#3,070 #1,656
  7. Computer Networks and Communications#873 #246
  8. Social Sciences#7,044 #4,443
all time2022–2025, better2022–2025, worse

A dot further right is a better standing. Fields it was not ranked in over the whole record are left out.

What does Alibaba Group (China) specialise in?

Where its research is concentrated relative to its size: Computer Vision and Pattern Recognition takes 15.4× the share of its output that it takes of world research.

  1. Computer Vision and Pattern RecognitionSubfield · 241.2 works15×
  2. Signal ProcessingSubfield · 44.1 works12×
  3. Artificial IntelligenceSubfield · 295.0 works9.8×
  4. Computer Networks and CommunicationsSubfield · 105.2 works9.5×
  5. Computer ScienceField · 843.9 works8.7×
← less than its size predictsmore →

Location quotient, a volume reading rather than an impact one. It surfaces small, lopsided specialities.

Field profile

Location quotient across every field it publishes in: outside the ring is more than an institution of this size would be expected to publish, inside it is less.

Agricultural and Biological Sciences: 0.09× its world share, 5 worksAgricultural and Bio…Biochemistry, Genetics and Molecular Biology: 0.27× its world share, 18 worksBiochemistry, Geneti…Immunology and Microbiology: 0.08× its world share, 1 worksImmunology and Micro…Neuroscience: 0.53× its world share, 11 worksNeurosciencePharmacology, Toxicology and Pharmaceutics: 0.13× its world share, 1 worksPharmacology, Toxico…Chemical Engineering: 0.02× its world share, 0 worksChemical EngineeringChemistry: 0.08× its world share, 2 worksChemistryComputer Science: 8.71× its world share, 844 worksComputer ScienceEarth and Planetary Sciences: 0.42× its world share, 8 worksEarth and Planetary …Energy: 0.23× its world share, 3 worksEnergyEngineering: 0.89× its world share, 166 worksEngineeringEnvironmental Science: 0.27× its world share, 17 worksEnvironmental ScienceMaterials Science: 0.08× its world share, 4 worksMaterials ScienceMathematics: 0.49× its world share, 7 worksMathematicsPhysics and Astronomy: 0.39× its world share, 11 worksPhysics and AstronomyDentistry: 0.21× its world share, 1 worksDentistryHealth Professions: 0.12× its world share, 4 worksHealth ProfessionsMedicine: 0.12× its world share, 29 worksMedicineNursing: 0.07× its world share, 0 worksNursingVeterinary: 0.10× its world share, 0 worksVeterinaryArts and Humanities: 0.08× its world share, 3 worksArts and HumanitiesBusiness, Management and Accounting: 0.75× its world share, 30 worksBusiness, Management…Decision Sciences: 2.44× its world share, 29 worksDecision SciencesEconomics, Econometrics and Finance: 0.23× its world share, 6 worksEconomics, Econometr…Psychology: 0.28× its world share, 11 worksPsychologySocial Sciences: 0.23× its world share, 36 worksSocial Sciencesworld share
more than its size predictsabout as predictedless

Rings at 0.5×, and 2×. Widest outward: Computer Science, 8.7×. Every wedge is a field page.

Who are the top researchers at Alibaba Group (China)?

Ranked on the composite score, Fei Sun, Liefeng Bo and Gang Wang lead among researchers whose main affiliation is Alibaba Group (China).

  1. 1 Fei Sun China · #28,919 worldwide 573 citations · 38 works
  2. 2 Liefeng Bo China · #32,034 worldwide 744 citations · 54 works
  3. 3 Gang Wang China · #39,233 worldwide 1.1k citations · 31 works
  4. 4 Jianqiang Huang China · #70,056 worldwide 418 citations · 37 works
  5. 5 Wei Lin China · #110,948 worldwide 223 citations · 29 works
  6. 6 Jingren Zhou China · #115,257 worldwide 487 citations · 96 works
  7. 7 Hongbo Deng China · #121,173 worldwide 137 citations · 34 works
  8. 8 Chang Zhou China · #124,375 worldwide 353 citations · 27 works
  9. 9 Tian Zhou China · #156,345 worldwide 229 citations · 30 works
  10. 10 Hongxia Yang China · #161,463 worldwide 509 citations · 87 works
All ranked researchers at Alibaba Group (China)

Which keywords describe research at Alibaba Group (China)?

By fractional works in 2022–2025, weighted toward what it does more of than the world: Deep Learning, Neural Networks, Machine Learning, Information Retrieval, Convolutional Neural Networks, Text Classification, Representation Learning and Distributed Systems.

▲ more of its work than of the world’s◆ about the world’s share▼ less than the world’s

Size is fractional works in 2022–2025 in the topics tagged with each word; colour is the word's share of this institution's work against its share of the world's. The 40 words are chosen for being large and distinctive. Each links to the topic it comes from most.

All 40 words, with their numbers
  1. Deep Learning409▲ 9.6×75 topics
  2. Neural Networks247▲ 13×32 topics
  3. Machine Learning120▲ 3.0×72 topics
  4. Information Retrieval74▲ 27×4 topics
  5. Convolutional Neural Networks73▲ 8.0×14 topics
  6. Text Classification64▲ 33×3 topics
  7. Representation Learning64▲ 37×3 topics
  8. Distributed Systems56▲ 27×6 topics
  9. Topic Modeling56▲ 30×3 topics
  10. Machine Translation55▲ 27×2 topics
  11. Semantic Similarity55▲ 37×1 topic
  12. Word Representation55▲ 37×1 topic
  13. Unsupervised Learning52▲ 29×3 topics
  14. Semi-Supervised Learning51▲ 37×3 topics
  15. Collaborative Filtering45▲ 101×1 topic
  16. Matrix Factorization45▲ 101×1 topic
  17. Big Data40▲ 3.5×18 topics
  18. Anomaly Detection38▲ 10×4 topics
  19. Graph Convolutional Networks37▲ 27×2 topics
  20. Convolutional Networks37▲ 21×3 topics
  21. Resource Management34▲ 16×3 topics
  22. Cloud Computing31▲ 8.9×8 topics
  23. Corpus Linguistics30▲ 11×2 topics
  24. Neural Machine Translation30▲ 25×1 topic
  25. Statistical Machine Translation30▲ 25×1 topic
  26. Semantic Reasoning29▲ 29×2 topics
  27. Visual Question Answering29▲ 29×2 topics
  28. Image Captioning29▲ 47×1 topic
  29. Multimodal Fusion29▲ 47×1 topic
  30. Virtualization29▲ 29×3 topics
  31. Heterogeneous Networks28▲ 24×2 topics
  32. Graph Neural Networks28▲ 48×1 topic
  33. Knowledge Graph Embedding28▲ 48×1 topic
  34. Meta-Learning26▲ 26×2 topics
  35. Object Recognition26▲ 28×2 topics
  36. Feature Matching26▲ 34×1 topic
  37. Local Descriptors26▲ 34×1 topic
  38. Parallel Computing25▲ 17×5 topics
  39. Data Centers25▲ 34×1 topic
  40. MapReduce25▲ 34×1 topic

Which research topics does Alibaba Group (China) publish most on?

By volume in 2022–2025: Topic Modeling, Recommender Systems and Techniques, Natural Language Processing Techniques and Multimodal Machine Learning Applications.

  1. 1 Topic Modeling Artificial Intelligence 55 works
  2. 2 Recommender Systems and Techniques Information Systems 45 works
  3. 3 Natural Language Processing Techniques Artificial Intelligence 30 works
  4. 4 Multimodal Machine Learning Applications Computer Vision and Pattern Recognition 29 works
  5. 5 Advanced Graph Neural Networks Artificial Intelligence 28 works
  6. 6 Advanced Image and Video Retrieval Techniques Computer Vision and Pattern Recognition 26 works
  7. 7 Cloud Computing and Resource Management Information Systems 25 works
  8. 8 Domain Adaptation and Few-Shot Learning Artificial Intelligence 21 works
  9. 9 Advanced Neural Network Applications Computer Vision and Pattern Recognition 21 works
  10. 10 Advanced Vision and Imaging Computer Vision and Pattern Recognition 16 works

How open and international is its research?

Against the world’s own shares — the tick on each track. Both are shares of its output, so they sit on one scale and can be read against each other as well as against the world.

openly available31.7%not open68.3%

World: 28% of research is openly available.

with international co-authors31.4%domestic only68.6%

World: 19% is written across borders.

How has Alibaba Group (China)'s research output changed?

Output in 2018–2022 was 771% higher than in 2013–2017.

1990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Other research institutions in Hangzhou

All research institutions in Hangzhou

Research measures only: rankings here say nothing about teaching, admissions or student experience. Comparable institutions and collaboration partners are in the interactive view on the map.