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Differences between journal and conference in computer science: a bibliometric view based on Bayesian network

Mingyue Sun

Mingyue Sun

  • Wuhan Library, Chinese Academy of SciencesHubei, China
  • Department of Information Resources Management, University of Chinese Academy of SciencesBeijing, China
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Sun, Mingyue
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Mingliang Yue

Mingliang Yue

  • Wuhan Library, Chinese Academy of SciencesHubei, China
  • Department of Information Resources Management, University of Chinese Academy of SciencesBeijing, China
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Yue, Mingliang
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Tingcan Ma

Tingcan Ma

  • Wuhan Library, Chinese Academy of SciencesHubei, China
  • Department of Information Resources Management, University of Chinese Academy of SciencesBeijing, China
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Ma, Tingcan
  
25 ago 2023
Differences between journal and conference in computer science: a bibliometric view based on Bayesian network's Cover Image
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Categoria dell'articolo: Research Paper
Pubblicato online: 25 ago 2023
Pagine: 47 - 60
Ricevuto: 28 apr 2023
Accettato: 22 mag 2023
DOI: https://doi.org/10.2478/jdis-2023-0017
Parole chiave
© 2023 Mingyue Sun et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Algorithm 1:

amended K2 algorithm
amended K2 algorithm

Figure 1.

The learned Bayesian network.
The learned Bayesian network.

Figure 2.

An example of Bayesian network inference by setting Category as journal.
An example of Bayesian network inference by setting Category as journal.

Figure 3.

The distribution of Category by setting various HIM and pNumM and auCDM.
The distribution of Category by setting various HIM and pNumM and auCDM.

Figure 4.

The distribution of Category by setting pNumM=(FiftyHundred, gtHundred), HIM=(FiftyHundred, gtHundred), auCDM=(mhigh,high).
The distribution of Category by setting pNumM=(FiftyHundred, gtHundred), HIM=(FiftyHundred, gtHundred), auCDM=(mhigh,high).

Figure 5.

mhigh or above CNCI probability by setting various Category and Rank.
mhigh or above CNCI probability by setting various Category and Rank.

Figure 6.

Distribution of refNum/abLen by setting various Category.
Distribution of refNum/abLen by setting various Category.

Figure 7.

mhigh or above pNov/pDisrupt probability by setting various Category.
mhigh or above pNov/pDisrupt probability by setting various Category.

Figure 8.

mhigh or above pNov/pDisrupt probability by setting various Category and Rank.
mhigh or above pNov/pDisrupt probability by setting various Category and Rank.

Discretization rules of factors (Sun et al_, 2023)_

Variable Discretization rule
pNov 0: zero; [0, 0.4]: low; (0.4, 0.6]; median; (0.6, 0.8]: mhigh; (0.8, 1]: high
pDisrupt <0: ngtzero; sort pDisrupt values and divide by top percentage interval: (70%, 100%]: low; (30%, 70%]: medium; (10%, 30%]: mhigh; (0%, 10%]: high
refNum [0, 10]: ltTen; (10, 20]: tenTwenty; (20, 30]: twentyThirty; > 30: gtThirty
abRE >70: easy; (50, 70]: medium; (40, 50]: mhard; (30, 40]: hard; <30: vhard (ref. Flesch, 1948)
abLen <600: short; (600, 800]; median; (800, 1000]: long; >1000: vlong
pNumF [0, 10]: ltTen; (10, 20]: tenTwenty; (20, 50]: twentyFifty; (50, 100]: FiftyHundred; > 100:
pNumM gtHundred
tcF [0, 10]: ltTen; (10, 100]: tenHundred; (100, 500]: HundredFiveH; (500, 2000]: fiveHTwentyH;
tcM (2000, 10000]: twentyHHundredH; > 10000: gtHundredH
HIF [0, 10]: ltTen; (10, 20]: tenTwenty; (20, 30]: twentyThirty; (30, 50]: thirtyFifty; (50, 100]:
HIM FiftyHundred
auCDFauCDMinstCDFinstCDM sort auCDF/auCDM values and divide by top percentage interval: (50%, 100%]: low; (20%, 50%]: mlow; (10%, 20%]: medium; (5%, 10%]: mhigh; (0%, 5%]: high
auNuminstNum 1: one; 2: tow; 3: three; 4: four; 5: five; >5: gtfive
CNCI (0, 0.3]: low; [0.3, 0.8]: mlow; (0.8, 1.2]; average; (1.2, 2]: mhigh; (2, 5]: vhigh; >5: exhigh
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Informatica, Tecnologia informatica, Project Management, Base dati e data mining
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