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Topic · Artificial Intelligence

Cognitive Computing and Networks

Cognitive Computing and Networks is a research topic within Artificial Intelligence. Science Explorer counts 7.8k research works in it since 1957. 12.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the theoretical framework of cognitive informatics, computational intelligence, and related fields. It covers topics such as semantic link networks, neural informatics, denotational mathematics, brain-inspired systems, cognitive computing, semantic analysis, knowledge representation, and the impact on the cyber-physical society.

  • Cognitive Informatics
  • Semantic Link Network
  • Computational Intelligence
  • Neural Informatics
  • Denotational Mathematics
  • Brain-Inspired Systems
  • Cognitive Computing
  • Semantic Analysis
  • Knowledge Representation
  • Cyber-Physical Society
Research works
7.8k
fractional, since 1957
In the world top 10%
964
per year above
Top-10% rate
12.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-10%
the tick is no change

Which countries lead Cognitive Computing and Networks research?

By volume, China and the United States publish the most (557 and 145 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 557 works
  2. 2 United States 145 works
  3. 3 India 139 works
  4. 4 Russia 70 works
  5. 5 United Kingdom 39 works
  6. 6 Germany 34 works
  7. 7 Japan 32 works
  8. 8 Poland 30 works
  9. 9 Ukraine 26 works
  10. 10 France 25 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 50.8%United States: 13.2%India: 12.6%Russia: 6.3%6 others listed: 17.0%51%largest
China557 · 50.8%United States145 · 13.2%India139 · 12.6%Russia70 · 6.3%6 others listed186 · 17.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Cognitive Computing and Networks research?

By volume in 2022–2025, Beijing University of Posts and Telecommunications publishes the most Cognitive Computing and Networks research, followed by Tsinghua University and National University of Singapore.

By volume, 2022–2025

  1. 1 Beijing University of Posts and TelecommunicationsChina 22 works
  2. 2 Tsinghua UniversityChina 11 works
  3. 3 National University of SingaporeSingapore 10 works
  4. 4 National University of Defense TechnologyChina 10 works
  5. 5 National Research Nuclear University MEPhIRussia 8 works
  6. 6 Zhejiang UniversityChina 8 works
  7. 7 Beijing University of TechnologyChina 8 works
  8. 8 Shanghai Jiao Tong UniversityChina 7 works
  9. 9 Beihang UniversityChina 7 works
  10. 10 Beijing Institute of TechnologyChina 7 works

Who are the leading researchers in Cognitive Computing and Networks?

The most-cited researchers publishing on Cognitive Computing and Networks include Witold Pedrycz and Dusit Niyato.

  1. 1 Witold Pedrycz Canada 5.4k citations
  2. 2 Dusit Niyato Singapore 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Cognitive Computing and Networks research done?

The largest centres of Cognitive Computing and Networks research in 2022–2025 are Beijing (China), Moscow (Russia), Nanjing (China) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Beijing and Xi'an.

Largest cities, 2022–2025

  1. 1 Beijing China 143 works
  2. 2 Moscow Russia 37 works
  3. 3 Nanjing China 34 works
  4. 4 Shanghai China 34 works
  5. 5 Xi'an China 31 works
  6. 6 Wuhan China 23 works
  7. 7 Hangzhou China 21 works
  8. 8 Guangzhou China 21 works
  9. 9 Changsha China 19 works
  10. 10 Chennai India 18 works

Where it is the local speciality

  1. BeijingCN · 142.8 works2.9×
  2. Xi'anCN · 30.5 works2.7×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Cognitive Computing and Networks than the world average.

See Cognitive Computing and Networks on the map

Where is the best place to study Cognitive Computing and Networks?

Among universities, judged by research, Beijing University of Posts and Telecommunications, Tsinghua University and National Research Nuclear University MEPhI score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 19.5%fractional works in this node (log) →share in the world top 10% →Beijing University of Posts and Telecommunications: 22, 30.2%Tsinghua University: 11, 40.6%National Research Nuclear University MEPhI: 8, 6.0%National University of Defense Technology: 10, 16.6%National University of Singapore: 10, 0.0%Zhejiang University: 8, 23.6%Tsinghua UniversityBeijing University o…National University …National Research Nu…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Beijing University of Posts and TelecommunicationsChina 68.630.2%24.6×22 -81.6%
2 Tsinghua UniversityChina 57.540.6%3.9×11 -30.7%
3 National Research Nuclear University MEPhIRussia 37.76.0%48.0×8 —
4 National University of Defense TechnologyChina 36.616.6%8.8×10 -69.0%
5 National University of SingaporeSingapore 33.60.0%7.6×10 -47.4%
6 Zhejiang UniversityChina 20.623.6%2.9×8 -64.1%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Cognitive Computing and Networks research growing?

Output in 2018–2022 was 10% lower than in 2013–2017, peaking in 2025.

19801990200020102020
grewheldshrank

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

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.