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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. 10.0% 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%
785
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+52%
the tick is no change
Largest producer
China
48% of the 10 largest, 2022-now

Which countries lead Cognitive Computing and Networks research?

By volume, China and the United States publish the most (626 and 206 works in 2022-now).

By volume, 2022-now

  1. 1 China 626 works
  2. 2 United States 206 works
  3. 3 India 173 works
  4. 4 Russia 75 works
  5. 5 United Kingdom 47 works
  6. 6 Germany 46 works
  7. 7 Japan 37 works
  8. 8 Poland 32 works
  9. 9 France 32 works
  10. 10 Ukraine 28 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: 48.1%United States: 15.8%India: 13.3%Russia: 5.8%6 others listed: 17.1%48%largest
China626 · 48.1%United States206 · 15.8%India173 · 13.3%Russia75 · 5.8%6 others listed222 · 17.1%

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

Which institutions lead Cognitive Computing and Networks research?

By volume in 2022-now, Beijing University of Posts and Telecommunications publishes the most Cognitive Computing and Networks research, followed by Chinese Academy of Sciences and Russian Academy of Sciences.

Who are the leading researchers in Cognitive Computing and Networks?

The most-cited researchers publishing on Cognitive Computing and Networks include Herbert A. Simon and H. Vincent Poor.

  1. 1 Herbert A. Simon United States 179k citations
  2. 2 H. Vincent Poor United States 165k citations

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

What are the most cited papers in Cognitive Computing and Networks?

The most cited paper in Cognitive Computing and Networks is "The Mathematical Theory of Communication" (Claude E. Shannon, Warren Weaver, Norbert Wiener, 1950), with 9,856 citations. Next come "ELIZA—a computer program for the study of natural language communication between man and machine" (4,420) and "Password authentication with insecure communication" (2,802).

Most cited papers
#PaperReleasedCitations
1 The Mathematical Theory of Communication Claude E. Shannon, Warren Weaver, Norbert Wiener · Physics Today Sep 1950 9,856
2 ELIZA—a computer program for the study of natural language communication between man and machine Joseph Weizenbaum · Communications of the ACM Open access 1966 4,420
3 Password authentication with insecure communication Leslie Lamport · Communications of the ACM Open access Nov 1981 2,802
4 The neural and computational bases of semantic cognition Matthew A. Lambon Ralph, Elizabeth Jefferies, Karalyn E. Patterson et al. · Nature reviews. Neuroscience ReviewOpen access 24 Nov 2016 2,045
5 Development and application of a metric on semantic nets Roy Rada, Hafedh Mili, Ellen J. Bicknell et al. · IEEE Transactions on Systems Man and Cybernetics 1989 1,832
All 98 most cited papers, with filters and sorting

Where is Cognitive Computing and Networks research done?

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

Largest cities, 2022-now

  1. 1 BeijingChina 154 works
  2. 2 MoscowRussia 39 works
  3. 3 ShanghaiChina 37 works
  4. 4 NanjingChina 37 works
  5. 5 Xi'anChina 34 works
  6. 6 WuhanChina 24 works
  7. 7 HangzhouChina 24 works
  8. 8 GuangzhouChina 24 works
  9. 9 ChennaiIndia 23 works
  10. 10 ChangshaChina 20 works

Where it is the local speciality

  1. ChennaiIN · 22.7 works2.7×
  2. BeijingCN · 153.9 works2.7×
  3. Xi'anCN · 34.2 works2.6×
← 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 Zhejiang University 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%10%20%30%mean 14.7%fractional works in this field (log) →share of work in the world top 10% →Beijing University o…Tsinghua UniversityZhejiang UniversityNational University …
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Beijing University of Posts and TelecommunicationsChina 91.326.7%25.3×24 +1144.5%
2Tsinghua UniversityChina 62.824.7%3.3×11 +491.1%
3Zhejiang UniversityChina 41.618.6%2.2×9 +425.7%
4National University of Defense TechnologyChina 36.412.4%9.2×11 +32.6%
5Shanghai Jiao Tong UniversityChina 31.515.2%1.9×8 +114.4%
6National University of SingaporeSingapore 30.83.4%5.9×10 -
7National Research Nuclear University MEPhIRussia 12.51.9%44.0×8 -24.3%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Cognitive Computing and Networks, country by country

Is Cognitive Computing and Networks research growing?

Output in 2022-2025 was 52% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Cognitive Computing and Networks.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Cognitive Computing and Networks as a percentage of every work published that year, 0.0087% in 2024.

still counting0.0050%0.010%0.015%Cognitive Computing and Networks0.0098% in 202519801990200020102020

Fractional works of Cognitive Computing and Networks over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Cognitive Computing and Networks10.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

Discussion

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