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Researcher · Baidu (China) · China

Xin Ya Song

Xin Ya Song publishes mostly in Artificial Intelligence, Information Systems and Electrical and Electronic Engineering, on topics such as Advanced Graph Neural Networks, Natural Language Processing Techniques and Topic Modeling.

ORCID 0000-0001-5571-3436 OpenAlex
World rank
#1,523,159
of 7,710,434 ranked researchers
Rank in China
#227,734
of 978,568
Works
36
Citations
471
Citations per work
13.1
h-index
10
i10-index 12Top 42% in Artificial Intelligence
Xin Ya Song

What does Xin Ya Song research?

Xin Ya Song's work is mostly in Computer Science (34%), Engineering (11%), Decision Sciences (6%).

Physical Sciences: 16 works · 45.0% of their workComputer Science: 12 works · 34.0% of their workArtificial Intelligence: 9 works · 26.0% of their workInformation Systems: 3 works · 8.0% of their workEngineering: 4 works · 11.0% of their workElectrical and Electronic Engineering: 2 works · 6.0% of their workControl and Systems Engineering: 2 works · 5.0% of their workEverything else: 16 works · 44.0% of their workSocial Sciences: 4 works · 11.0% of their workDecision Sciences: 2 works · 6.0% of their workManagement Science and Operations Research: 2 works · 6.0% of their workBusiness, Management and Accounting: 2 works · 5.0% of their workOrganizational Behavior and Human Resource Management: 2 works · 5.0% of their workTHEIR WORKXin Ya Song36works
Physical Sciences45%Social Sciences11%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science1234.0%
    2. Engineering411.0%
  2. Everything else1644.0%
    1. Decision Sciences26.0%
    2. Business, Management and Accounting25.0%
  1. 1Artificial Intelligence26% of their works
  2. 2Information Systems8% of their works
  3. 3Electrical and Electronic Engineering6% of their works
  4. 4Management Science and Operations Research6% of their works
  5. 5Organizational Behavior and Human Resource Management5% of their works
  6. 6Control and Systems Engineering5% of their works

Research topics

Which keywords describe Xin Ya Song's research?

The keywords of their largest research topics: Deep Learning, Graph Neural Networks, Knowledge Graph Embedding, Machine Learning, Neural Networks, Affective States, Big Data and Collaborative Filtering.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 19 words, with their numbers
  1. Deep Learning92 topics
  2. Graph Neural Networks61 topic
  3. Knowledge Graph Embedding61 topic
  4. Machine Learning62 topics
  5. Neural Networks62 topics
  6. Affective States31 topic
  7. Big Data31 topic
  8. Collaborative Filtering31 topic
  9. Community Structure31 topic
  10. Educational Data Mining31 topic
  11. HR Analytics31 topic
  12. Intelligent Tutoring Systems31 topic
  13. Learning Analytics31 topic
  14. Machine Translation31 topic
  15. Matrix Factorization31 topic
  16. Network Dynamics31 topic
  17. Neural Machine Translation31 topic
  18. Statistical Machine Translation31 topic
  19. Word Representation31 topic

Where does Xin Ya Song work?

Xin Ya Song's main affiliation in the publication record is Baidu (China), China.

How many publications and citations does Xin Ya Song have?

OpenAlex counts 36 works and 471 citations for Xin Ya Song, an h-index of 10, ranking #1,523,159 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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