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

Mingyue Sun
Mingyue Sun
, 
Mingliang Yue
Mingliang Yue
 and 
Tingcan Ma
Tingcan Ma
   | Aug 25, 2023
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Article Category: Research Paper
Published Online: Aug 25, 2023
Page range: 47 - 60
Received: Apr 28, 2023
Accepted: May 22, 2023
DOI: https://doi.org/10.2478/jdis-2023-0017
Keywords
© 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
eISSN:
2543-683X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining
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