Have a personal or library account? Click to login
Differences between journal and conference in computer science: a bibliometric view based on Bayesian network Cover

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

Open Access
|Aug 2023

Figures & Tables

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)_

VariableDiscretization rule
pNov0: 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:
pNumMgtHundred
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]:
HIMFiftyHundred
auCDFauCDMinstCDFinstCDMsort auCDF/auCDM values and divide by top percentage interval: (50%, 100%]: low; (20%, 50%]: mlow; (10%, 20%]: medium; (5%, 10%]: mhigh; (0%, 5%]: high
auNuminstNum1: 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
DOI: https://doi.org/10.2478/jdis-2023-0017 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 47 - 60
Submitted on: Apr 28, 2023
Accepted on: May 22, 2023
Published on: Aug 25, 2023
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Mingyue Sun, Mingliang Yue, Tingcan Ma, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.