Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
39,
No.
2,
August
2025,
pp.
973
∼
986
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v39.i2.pp973-986
❒
973
CriteriaCheck
er:
a
kno
wledge
graph
appr
oach
to
enhance
integrity
and
ethics
in
academic
publication
Garima
Sharma
1
,
V
ikas
T
ripathi
1
,
V
ijay
Singh
2
1
Department
of
Computer
Science
and
Engineering,
Graphic
Era
Deemed
to
be
Uni
v
ersity
,
Dehradun,
India
2
Cisco-NUS
Corporate
Lab,
National
Uni
v
ersity
of
Sing
apore,
K
ent
Ridge,
Sing
apore
Article
Inf
o
Article
history:
Recei
v
ed
Apr
4,
2024
Re
vised
Mar
17,
2025
Accepted
Mar
26,
2025
K
eyw
ords:
Centrality
graph
analytics
Information
e
xtraction
Kno
wledge
graph
Le
gitimate
publishers
Predatory
criteria
ABSTRA
CT
Academic
writing
is
an
inte
gral
part
of
scientic
communities.
This
is
a
for
-
mal
s
tyle
of
writing
used
by
researchers
and
scholars
to
communicate
critical
analysis
and
e
vidence
based
ar
guments.
This
w
ork
sho
wcased
a
graph-based
approach
for
scraping,
e
xtracting,
representing
and
e
v
aluating
the
a
v
ailable
aca-
demic
writing
for
gery
detection
criteria
and
further
enhanci
ng
the
model
by
proposing
a
set
of
ne
w
age
criteria.
The
proposed
w
ork
is
based
on
kno
wledge
graphs
and
graph
analytics
capable
of
selecting
subset
of
16
criteria
from
the
a
v
ailable
superset
of
a
cent
of
criterias
pro
vided
by
Bealls,
Cabells,
Shreshtha,
and
Think.Check.Submit,
Scopus,
and
other
rele
v
ant
authors.
The
process
for
detecting
the
inuencial
parameters
cons
ists
of
04
phases:
dataset
preparation,
kno
wledge
graph
representation
and
making
inferences
through
graph
analyt-
ics
and
e
v
aluation
of
results.
The
e
xperimental
results
are
then
compared
to
the
retraction
database
that
consisting
of
information
about
retracted
articles.
The
w
ork
enables
the
construction
of
an
e
xperiential
kno
wledge
graph
that
ef-
fecti
v
ely
identies
inue
ntial
criteria,
enhancing
this
list
by
incorporating
ne
w
age
criteria
into
current
inuential
set
and
concluding
in
result
by
successfully
detecting
the
academic
predatory
beha
vior
.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Garima
Sharma
Department
of
Computer
Science
and
Engineering,
Graphic
Era
deemed
to
be
Uni
v
ersity
Dehradun,
Uttarakhand,
India
Email:
g
arima
vrm91@gmail.com
1.
INTR
ODUCTION
An
unethical
prac
ticing
journal
[1]
commonly
kno
wn
as
predatory
journal
gets
associated
with
some
suspicious
publisher
called
as
predatory
publisher
[2]
represents
an
e
xploited
publishing
model
in
academics.
The
characteristics
of
a
predatory
publisher
comprise
e
xpedited
re
vie
ws,
lacking
professional
re
vie
w
mecha-
nisms,
decepti
v
e
impact
f
actors,
f
alsely
listed
respected
scientists
on
editorial
boards,
an
e
xtensi
v
e
repository
of
articles,
journal
titles
that
mimic
those
of
reputable
journals,
and
persistent
spam
in
vitations
ur
ging
arti-
cle
submissions
[3].
Predatory
publishing
has
become
more
widespread
issue
that
is
ne
g
ati
v
ely
impacting
academics
as
well
as
research
inte
grity
,
and
therefore
dissemination
of
inappropriate
kno
wledge
in
dif
ferent
sectors
[4].
One
major
concern
f
acing
the
academic
research
community
is
the
proliferation
of
misinforma-
tion
and
disinformation
resulting
from
unethical
publication
practices.
In
the
current
en
vironment,
publishing
houses
frequently
o
v
erlook
le
gitimate
content
concerns
in
f
a
v
our
of
commercial
considerations.
The
y
claim
to
adhere
to
genuine
academic
protocols
for
closely
e
xamining
research,
b
ut
the
y
routinely
generate
articles
that
are
poorly
produced,
f
all
outside
of
their
purvie
w
,
and
contain
glaringly
frequent
errors
or
re
v
ersals
of
impact
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
974
❒
ISSN:
2502-4752
f
actors.
It
erodes
condence
in
scientic
publications
as
a
result.
A
pioneering
compan
y
,
named
retraction
w
atch
[5],
is
at
the
forefront
of
a
re
v
olutionary
initiati
v
e
set
to
identify
and
retract
predatory
publications.
This
compan
y
has
coined
a
ne
w
term,
“paper
mill,
”
and
has
designated
Hinda
wi
as
a
leader
in
the
paper
mill
industry
[6].
The
disco
v
eries
made
by
independent
researchers
indicated
that
the
inltration
of
Hinda
wi
special
issues
occurred
on
a
lar
ger
scale
than
initially
e
xpected.
Thus
making
it
one
of
the
parameters
for
the
identication
of
ne
w
age
predatory
.
The
analysis
concluded
with
retract
ion
of
more
than
8,000
papers
ha
ving
included
se
v
eral
parameters
for
predatory
identication
including
issues
in
scope,
research
description,
data
a
v
ailability
,
cita-
tions,
coherence,
and
peer
-re
vie
w
inte
grity
,
indicating
potential
problems
with
the
quality
and
reliability
of
the
reported
research
[6].
W
ith
e
v
ery
research
in
this
area,
the
author
does
encounter
a
fe
w
common
and
impor
-
tant
aspects
to
easily
nd
the
suspicious
ones.
The
common
practices
emplo
yed
by
such
predators
consititues
unsolicited
‘spam’
emails,
char
ging
author’
s
high
publication
fees
without
conducting
thorough
assessments
of
articles
for
their
quality
and
le
gitimac
y
[7].
Predatory
publishers
e
v
en
emplo
y
tactics
such
as
distorting
peer
re
vie
w
processes,
misrepresenting
editorial
services,
and
f
alsely
claiming
database-inde
xing
statuses
[8].
From
f
alsifying
the
inde
x
es
to
for
ging
the
impact
f
actor
v
alues
with
high
arti
cle
processing
char
ges
(APC),
the
a
w
areness
between
correct
inde
xing,
ranking,
editorial
boards,
and
membership.
Can
help
the
researchers
to
refrain
from
ille
gitimate
journals
as
well
as
publishers.
Ce
rtainly
,
these
j
ournal
types
are
inf
amous
for
emplo
y-
ing
decepti
v
e
tactics
to
entice
researchers
into
submitting
manuscripts,
later
imposing
e
xcessi
v
e
APC
before
publication
leading
to
decei
ving
no
vices
[9].
Re
g
ardless
of
their
mode
of
operation,
open
or
not,
the
predatories
lags
in
fullli
ng
the
lack
of
le
g
al
and
essential
editorial
as
well
as
publishing
services.
Earl
y
career
researchers
(ECR)
are
especially
prone
to
f
all
victim
to
thes
e
tactics,
gi
v
en
the
challenges
the
y
f
ace
in
securing
emplo
yment
and
promotions
[10].
In
2017,
Bealls
shared
a
report
to
standardized
a
set
of
criterias
to
cate
gorize
predators
[11].
He
continues
the
updation
of
this
set
for
another
5
years
b
ut
discontinued
this
due
to
backslash
from
v
arious
publishers
and
a
fe
w
other
unkno
wn
reasons
[4].
The
establishment
of
Cabell’
s
whitelist
and
blacklist
in
2018
[12],
Jiban
Shrestha’
s
set
of
predatory
criteria
in
2021
[13],
re
gularly
updated
criteria
from
Scopus
[14],
other
authors
[15]
and
[16],
and
public
research
communities
[17]
are
just
a
fe
w
of
the
ongoing
ef
forts
to
identify
predatory
publishing
that
ha
v
e
surf
aced
since
Bealls
shutdo
wn.
The
practice
of
publishing
has
signi
cantly
increased
as
a
reason
of
educational
reforms
in
v
arious
de-
v
eloping
countri
es
[18],
with
increasing
rates
ranging
from
10%
to
16%
[19]
and
e
v
en
higher
today
.
Countries
hea
vily
implicated
in
these
unethical
practices
include
the
US,
China,
German
y
,
and
the
UK.
A
core-periphery
netw
ork
dynamics
may
be
e
vident
among
de
v
eloping
nations
[20].
A
substantial
reason
for
this
rise
is
particu-
larly
notable
in
countries
that
ha
v
e
implemented
signicant
structural
funding
reforms
in
the
past
tw
o
decades,
such
as
China
(2002),
Norw
ay
(2003),
Russia
(2005),
and
German
y
(2006).
The
e
xamination
of
the
timeline
of
distractions
and
missed
opportunities
since
Jef
fre
y
Beall
alerted
to
the
risks
associated
with
pseudo-publishers
and
identied
the
majority
of
those
operating
at
that
time
is
been
highlighted
by
Do
wnes
[21].
Using
a
combina-
tion
of
Beall’
s
list
and
predatory
publisher
data
supplied
by
researchers,
an
online
plug-in
from
ispredatory
.com
emplo
ys
cro
wdsourcing
[22].
Users
can
retrie
v
e
a
manually
updated
list
of
v
eried
predatory
publishers
and
search
for
publishers
by
name,
URL,
title,
or
journal
ISSN.
According
to
Cabells’
Predatory
Reports
database
in
2021,
around
15,000
predatory
journals
were
acti
v
e,
leading
authors
to
collecti
v
ely
pay
hu
ndre
ds
of
thou-
sands
of
dollars
to
publish
their
papers.
The
In
v
estig
ation
of
the
incursion
of
journals
with
suspected
predatory
practices
into
the
citation
database
Scopus
and
e
xplores
v
ariations
across
countries
in
scholars’
lik
elihood
to
publish
in
such
journals
is
sho
wcased
by
Mach
´
a
ˇ
cek
[23].
Prakash
et
al.
[24]
e
xplores
potential
predatory
jour
-
nals
and
those
with
poor
scientic
standards
by
analyzing
citations
to
124
such
journals
in
Scopus.
This
study
e
xplores
the
geographic
location,
publi
cations,
and
citations
of
citing
authors.
The
ndings
indicate
that
the
characteristics
of
citing
authors
ha
v
e
a
close
resemblance
with
those
of
the
publishing
authors
in
these
journals.
In
one
of
the
w
ork
[25],
the
author
e
xplores
mentoring
approaches
for
guiding
graduate
students
in
a
v
oiding
predatory
publications
and
dubious
conferences.
These
conferences
often
of
fer
swift
manuscript
re
vie
w
pro-
cesses,
commonly
omitting
the
f
act
that
the
y
de
viate
from
standard
peer
-re
vie
w
protocols
[8].
There
are
ne
w
approaches
that
authors
are
nding
no
w
adays
to
detect
predatory
publications.
An
open
automation
system
for
identifying
predatory
journals
is
been
proposed
by
[26].
This
AI-enabled
system
uses
Feature
Extraction
and
a
Bag
of
w
ords
algorithm
to
distinguish
between
le
git
imate
and
predatory
publishers.
In
one
e
xample,
the
connections
between
indi
vidual
articles
and
predatory/le
gitimat
e
publishers
and
journals
are
analyzed
while
emplo
ying
a
data-dri
v
en
training
model
named
PredCheck
[26].
F
or
an
y
researcher
,
therefore,
it
is
a
matter
of
utmost
importance
to
de
v
elop
the
right
understanding
of
dif
ferentiating
betwee
n
ethical
and
unethical
publishers.
Identication
of
predatory
publishers
can
be
done
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
39,
No.
2,
August
2025:
973–986
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
975
using
v
arious
parameters
such
as
editors’
suspicious
role
during
publication
[27],
manuscript
writing
le
v
el
features
[28],
claimed
to
be
peer
-re
vie
wed
[29],
sending
emails
to
resea
rchers
in
an
attempt
to
publish
articles
[30],
f
alsely
claiming
to
maintain
adequate
quality
control,
pro
viding
subpar
editorial
services
and
inadequate
cop
y
editing,
all
while
imposing
undisclosed
and
e
xcessi
v
e
publication
fees
on
the
researchers
[31],
etc.
This
study
p
os
sess
a
comprehensi
v
e
comprehension
and
recognition
of
k
e
y
parameters
for
swiftly
and
ef
fortlessly
identifying
predatory
practices.
It
is
vital
to
identify
the
source
s
and
tak
e
appropriate
action
on
a
global
and
re
gional
scale
ag
ainst
publishers
eng
aged
in
ille
g
al
or
unethical
practices.
While
other
studies
conned
their
reliability
solely
on
Beall’
s
list
or
completely
disre
g
arding
it
is
insuf
cient
to
address
the
issue
ef
fecti
v
ely
.
Numerous
researchers
are
acti
v
ely
in
v
estig
ating
the
k
e
y
parameters
of
research
publication
fraud.
T
eix
eira
Da
Silv
a
[27],
Machacek
primarily
relies
on
the
Scopus
database
to
identify
and
label
journals
as
predatory
.
Ho
we
v
er
,
this
analysis
o
v
erlooks
the
inte
gration
of
other
reputable
databases
such
as
W
OS,
UGC,
MAKG,
Publons,
and
predatory
databases
lik
e
Beall’
s.
Do
wnes
[20]
e
xplores
the
lik
elihood
of
a
journal
being
both
open-access
and
predatory
simultaneously
.
The
researc
her
depends
on
Beall’
s
library
and
four
prominent
databases—W
eb
of
Science,
Scopus,
Dimensions,
and
Microsoft
Academics—as
the
primary
and
e
xclusi
v
e
means
of
comprehensi
v
ely
analyzing
and
cate
gorizing
open-access
journals
as
predatory
or
not.
The
author
hea
vily
leans
on
Beall’
s
library
,
b
ut
this
reliance
is
considered
inaccurate
due
to
the
library’
s
f
ailure
to
pro
vide
scientic
reasoning
for
cate
gorizing
an
y
journal
into
the
distrustful
cate
gory
.
Instead,
Beall’
s
library
is
criticized
for
presenting
a
list
of
baseless
alle
g
ations
when
assessing
journals,
publishers,
and
v
arious
de
v
eloping
re
gions
such
as
Asia
and
Africa.
There
are
se
v
eral
parameters
in
circulation
claimed
to
be
ef
fecti
v
e
in
the
identication
of
predatory
in
academic
writing.
Each
author
has
presented
a
distinguished
methodology
and
set
of
criteria.
Pro
viders
include
Beall,
Cabell,
Public
Research
Communities,
Shrestha,
Scopus,
and
Think.Check.Submit
and
other
authors.
A
comparati
v
e
study
between
Bealls
and
Cabells
has
been
demonstrated
in
T
able
1.
T
able
1.
Comparati
v
e
analysis
of
tw
o
prominent
predatory
criteria
pro
viders
P
arameter
Bealls
Cabells
Output
List
of
journals/publishers
practicing
predatory/suspicious
practices
Extensi
v
e
information
about
v
arious
journal
types,
their
suitability
,
range
of
quality
metrics
Last
update
2017,
2021
Up
to
date
Subscription
No
Y
es
Usability
Predatory
practi
ce
only
Suitability
of
journal/publisher
for
publication.
F
ocus
Predatory
or
possible
predatory
journals/publishers
Ev
aluation
of
journals
on
metrics
Methodology
V
ague
W
ell-dened
Metrics
Non-systema
tic
Systematic
A
v
ailability
Free
to
acces
s
P
aid
access
Maintainability
P
assi
v
e
Acti
v
e
Commercial
service
No
Y
es
Since
there
are
hundreds
of
w
ays
specied
by
dif
ferent
criteria
pro
viders
hence
there
is
a
need
to
summarize
them
and
nd
the
most
inuential
ones
that
can
guide
the
researcher
at
early
stage
of
publication
as
this
has
not
be
e
xplicitly
addressed
to
date.
The
present
w
ork
aims
to
de
v
elop
such
intuiti
v
e
set
of
criteria
from
the
collection
of
more
than
hundred
criteria
pro
vided
by
abo
v
e
me
ntioned
authors.
Our
proposed
solution
in-
v
olv
es
a
model
dening
and
utilizing
predat
ory
and
le
gitimate
criteria
constructed
from
dif
ferent
le
gitimate
and
predatory
journal
websites
using
web
scraping
of
websites
of
dif
ferent
criteria
pro
viders.
The
collected
data
is
then
pro
vided
the
weights
based
upon
the
frequenc
y
of
their
occurrence
at
dif
ferent
instances
so
collected
using
the
higher
the
occurrence,
the
higher
the
weighting
methodology
.
Thi
s
weighted
m
atrix
is
used
to
const
ruct
a
kno
wledge
graph
[32],
[33]
to
obtain
a
consolidated
graph
using
t
riplet
as
Le
v
el,
P
arameter
,
P
aram
Pro
vider
.
W
e
analyzed
this
graph
using
centrality
analytics
[34]
to
nd
the
most
inuential
nodes
among
these.
W
e
concluded
our
w
ork
by
specifying
16
criteria
inuencing
more
than
100
of
the
criteria
pro
vided
by
dif
ferent
pro
viders
at
dif
ferent
le
v
els.
W
ith
this
recommendation,
we
ha
v
e
also
identied
12
ne
w
parameters
to
enhance
the
o
v
erall
model
for
the
identication
of
ne
w-era
suspicious
journals.
The
remainder
of
this
paper
follo
ws
a
sequential
structure
k
eeping
the
rst
section
in
order
is
pre-
sentation
of
the
main
theoretical
concepts
related
to
le
gitimate
and
ille
gitimate
publishers
characteristics
along
with
the
w
ork
done
by
dif
ferent
authors
so
f
ar
in
detecting
the
predatory
.
In
the
ne
xt
section,
we
then
intro-
duce
our
proposed
research
methodology
of
e
xtracting
the
parameter
,
de
v
eloping
a
kno
wledge
graph,
nding
the
inuential
parameters
and
e
v
aluating
the
results
with
and
without
ne
w
parameters.
In
the
third
section,
CriteriaChec
k
er:
a
knowledg
e
gr
aph
appr
oac
h
to
enhance
inte
grity
...
(Garima
Sharma)
Evaluation Warning : The document was created with Spire.PDF for Python.
976
❒
ISSN:
2502-4752
we
then
sho
wcased
the
e
xperimental
results
and
the
discussions
displaying
the
inuential
parameters
and
a
winner
of
ille
gitimate
spreader
.
In
the
last
section,
we
ha
v
e
presente
d
the
conclusion
and
future
w
ork
on
the
e
xtension
of
the
presented
w
ork.
2.
METHOD
In
this
w
ork,
the
criteria
checking
model
is
prepared
and
implemented
to
identify
the
inuential
param-
eters
from
a
set
of
predatory
detection
criteria
established
by
v
arious
authors.
The
proposed
model,
illustrated
in
Figure
1,
comprises
of
four
primary
phases,
each
encompassing
a
distinct
set
of
tasks.
Figure
1.
Proposed
o
v
erall
architecture
W
e
started
the
data
collection
process
through
W
eb
Scraping
approach.
The
authors’
data
has
es-
sentially
been
web-scraped
programmaticaly
using
re
ge
x
and
other
functions
which
then
been
correlated
with
their
a
v
ailable
parameters.
The
e
xtracted
data
w
as
subsequently
correlated
with
the
a
v
ailable
parameters
to
en-
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
39,
No.
2,
August
2025:
973–986
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
977
sure
coherence
and
v
alidity
.
This
informati
on
w
as
then
structured
into
a
kno
wledge
graph,
wherein
each
node
represents
the
e
xtracted
parameters,
while
the
edges
denote
the
weighted
relationships
between
the
le
v
el
type
and
the
criteria
pro
vider
.
The
le
v
el
types
analyzed
in
this
study
include
journals,
publishers,
and
conferences.
Through
graph
analytics,
our
ndings
indicate
that
the
highest
de
gree
of
for
gery
occurs
at
the
journal
le
v
el.
F
or
this
purpose,
the
comprehensi
v
e
analysis
has
been
systematically
di
vided
into
four
major
sections:
2.1.
Data
pr
eparation
The
de
v
elopment
of
a
rob
ust
kno
wledge
graph
requires
met
iculous
data
preparation
to
ensure
accu-
rac
y
,
consistenc
y
,
and
semantic
richness.
Initially
,
the
scope
of
the
graph
w
as
dened
by
identifying
rele
v
ant
entities,
relationships,
and
attrib
utes
aligned
with
the
intended
application
domain.
The
typical
three
steps
to
achie
v
e
this
are
as
follo
ws:
2.1.1.
Identication
of
criteria
pr
o
viders
This
pi
v
otal
stage
in
the
entire
algorithm’
s
operation
and
design
serv
es
as
a
crucial
data
collection
uni
t.
During
this
step,
v
arious
automated
techniques
are
emplo
yed
to
retrie
v
e
the
signicant
parameter
pro
viders
for
subsequent
parametric
analysis.
W
e
ha
v
e
de
v
eloped
our
web
scrapping
engine
and
e
xtracted
the
details
of
v
arious
authors
w
orking
on
parametric
designs
for
predatory
publications.
Since
the
data
g
athere
d
w
as
huge
in
size
we
prepared
and
sa
v
ed
the
same
in
the
graph
database,
required
for
graph
analytics.
Further
,
we
e
xtracted
and
added
the
characteristics
of
v
arious
predatory
publishers
using
web
mining
techniques
and
identied
k
e
y
parameter
pro
viders
useful
for
the
model
design
and
further
analysis.
2.1.2.
Actual
parameter
extraction
Using
mark
et
bask
et
analysis,
we
ha
v
e
identied
the
frequent
characteristi
cs
and
common
parameters
as
discussed
by
dif
ferent
authors
in
the
pre
vious
step.
The
actual
parameters
e
xtracted
is
more
than
a
100
while
a
fe
w
of
them
has
been
listed
in
T
able
2
belo
w
wherein
J
stands
for
journal
le
v
el
and
P
stands
for
Publisher
le
v
el
f
alsication.
T
able
2.
Glimpse
of
e
xtracted
parameters
(P)
catalogue
as
pro
vided
by
dif
ferent
pro
viders
PNo
Le
v
el
PDetails
Bealls
PRC
Cabells
Shreshtha
Scopus
Think.Check.Submit
Others
1
J/P
Soliciting
authors
for
publication
vi
a
emails
F
T
T
T
F
F
T
2
J/P
Luring
authors
for
f
ast
publication
vi
a
emails
T
T
F
T
F
T
T
3
J/P
Requesting
high/v
ery
lo
w
publicat
ion
char
ges
after
re
vie
w
T
T
F
T
F
T
T
4
J
Claimed
to
be
peer
re
vie
wed
F
T
T
T
T
T
T
5
J
Short
re
vie
w
timing
T
T
T
T
T
T
T
6
J/P
Bogus
impact
f
actors
are
GIF
,
Inde
x
Copernicus
v
alue,
Citef
actor
,
UIF
F
T
F
F
F
F
F
7
J/P
F
alsify
le
gitimate
impact
f
actors
F
T
F
F
F
F
T
8
J/P
V
eried
impact
f
actors
are
Google,
Di-
mensions,
and
W
eb
of
Science
F
T
F
F
F
F
F
9
J
Editors
lack/for
ging
qualications
in
the
eld
F
T
T
F
F
T
T
10
P
Dif
ferent
journal,
single
publi
sher
,
same
editorial
board
F
T
F
F
F
T
F
2.1.3.
Fr
equency
weight
assignment
This
section
forms
the
core
of
the
operational
principle
for
the
proposed
and
implemented
model.
The
minimum
weight
assigned
to
an
y
parameter
is
0
and
the
maximum
goes
up
to
3.
The
weight
depends
upon
the
pre
vious
section
as
the
frequenc
y
of
characteristics
present
in
a
parameter
pro
vider’
s
catalog
is
directly
proportional
to
the
weight
assigned
to
it.
x
′
=
n
X
i
=0
x
i
(1)
Using
abo
v
e,
the
updated
table
has
been
sho
wcased
in
T
able
3.
CriteriaChec
k
er:
a
knowledg
e
gr
aph
appr
oac
h
to
enhance
inte
grity
...
(Garima
Sharma)
Evaluation Warning : The document was created with Spire.PDF for Python.
978
❒
ISSN:
2502-4752
T
able
3.
Glimpse
of
updated
parameters
(P)
catalogue
after
weight
assignment
PNo
Le
v
el
PDetails
Bealls
PRC
Cabells
Shreshtha
Scopus
Think.Check.Submit
Others
1
J/P
Soliciting
authors
for
publication
via
emails
0
1
3
1
0
0
1
2
J/P
Luring
authors
for
f
ast
publication
via
emails
1
1
0
1
0
1
1
3
J/P
Requesting
high/v
ery
lo
w
publi
cation
char
ges
after
re
vie
w
1
1
0
1
0
1
1
4
J
Claimed
to
be
peer
re
vie
wed
0
1
2
1
1
1
1
5
J
Short
re
vie
w
timing
2
1
3
1
1
1
1
6
J/P
Bogus
impact
f
actors
are
GIF
,
Inde
x
Copernicus
v
alue,
Citef
actor
,
UIF
0
1
0
0
0
0
0
7
J/P
F
alsify
le
gitimate
impact
f
actors
0
1
0
0
0
0
1
8
J/P
V
eried
Impact
F
actors
are
Google,
D
i-
mensions,
and
W
eb
of
Science
0
1
0
0
0
0
0
9
J
Editors
lack/for
ging
qualications
in
the
eld
0
1
2
0
0
1
1
10
P
Dif
ferent
journal,
single
publ
isher
,
same
editorial
board
0
1
0
0
0
1
0
2.2.
Kno
wledge
graph
and
infer
ences
In
the
ne
xt
step,
a
global
kno
wledge
graph
w
as
constructed
by
e
xtracting
entities,
relationships,
and
attrib
utes
from
curated
datasets,
follo
wed
by
data
cleaning,
normalization,
and
alignment
of
the
ontology
to
ensure
semantic
consistenc
y
.
The
processed
data
were
transformed
into
a
graph-based
representation,
enabling
a
structured
inte
gration
of
the
collected
heterogeneous
sources.
In
addition,
centrality
measures
(e.g.,
de
gree,
betweenness,
and
closeness)
were
applied
to
assess
the
relati
v
e
importance
of
nodes
and
parameters
within
the
graph.
These
analytics
f
acilitated
the
identication
of
k
e
y
entities
inuencing
netw
ork
connecti
vity
and
supported
subsequent
inference
generation.
The
detailed
steps
are
mentioned
belo
w:
2.2.1.
Kno
wledge
graph
pr
eparation
A
structured
representation
of
captured
information
from
the
abo
v
e
sections
is
prepared
using
a
kno
wl-
edge
graph
approach
wherein
each
le
v
el
is
a
node
and
all
the
weights
act
as
edges
to
the
node.
The
typical
algorithm
follo
wed
in
the
de
v
elopment
of
the
weighted
graph
is
gi
v
en
in
Algorithm
1.
Algorithm
1
Create
a
weighted
kno
wledge
graph
1:
Input:
Data
D
containing
entities
and
relationships.
2:
Output:
Graph
G
with
weighted
edges.
3:
Initialize
an
empty
graph
G
=
{}
4:
Step
1:
Extract
Entities
5:
Extract
the
set
of
entities
E
from
the
input
data
D
6:
f
or
each
entity
e
∈
E
do
7:
Add
node
e
to
the
graph
G
8:
end
f
or
9:
Step
2:
Identify
Relationships
Between
Entities
10:
f
or
each
pair
of
entities
e
1
,
e
2
∈
E
do
11:
if
e
1
̸
=
e
2
then
12:
Calculate
relationship
strength
w
between
e
1
and
e
2
13:
if
w
>
threshold
then
14:
Add
edge
between
e
1
and
e
2
with
weight
w
15:
end
if
16:
end
if
17:
end
f
or
18:
Step
3:
Retur
n
the
Graph
19:
Return
the
graph
G
W
e
visualize
the
graph
obtained
in
the
spring
layout
as
sho
wn
in
belo
w
Figure
2.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
39,
No.
2,
August
2025:
973–986
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
979
Figure
2.
Kno
wledge
graph
representation
about
the
relationship
between
criteria
and
le
v
els
2.2.2.
Centrality
analytics
among
parameters
Centrality
analytics
helps
in
measuring
the
inuential
nodes
within
a
graph
and
helps
to
identify
es-
sential
edges
within
a
netw
ork
of
information
[34].
The
higher
de
gree
of
centrality
means
the
higher
connected
node.
The
highest
de
gree
nodes
ha
v
e
been
k
ept
in
the
se
v
ere
cate
gory
,
the
a
v
erage
lik
ed
nodes
ha
v
e
been
put
into
the
moderate
cate
gory
,
and
the
least
link
ed
into
the
lo
west
cate
gory
.
Figure
02
sho
wcases
ho
w
the
journal
type
node
becomes
the
center
node
of
all
the
parameters
where
publisher
-le
v
el
and
indi
vidual-le
v
el
nodes
ha
v
e
a
fe
w
connections.
d
′
=
N
X
i
=0
d
i
/
N
(2)
Wherein,
d
i
=
number
of
edges
connected
to
node
i,
N
=
total
number
of
nodes.
The
proposed
met
hod
tended
to
observ
e
the
cate
gory
of
le
v
el
at
which
for
gery
is
happening.
While
analyzing
the
updated
parameters
it
has
been
assessed
that
these
cat
e
gori
es
of
for
ging
belong
to
a
specic
le
v
el
such
as
journal
or
publisher
or
indi
vidual
wherein
fe
w
of
them
are
common
between
these
le
v
els.
K
eeping
this
in
vie
w
,
a
kno
wledge
graph
so
that
the
relationship
connection
between
these
parameters
can
be
performed.
Our
graph
triplets
<
subjects,
predicate
>
consisted
of
<
le
v
el,
parameter
,
each
parameter
pro
vider
>
ha
ving
weights
assigned
to
each
parameter
as
per
the
T
able
3.
The
combination
of
these
triplets
formed
a
netw
ork
of
interconnected
information
is
sho
wn
in
Figure
2.
3.
RESUL
TS
AND
DISCUSSION
Our
proposed
approach
through
centrality
analytics
of
kno
wledge
graphs,
w
as
capable
of
nding
the
most
inuential
parameters
that
directly
promotes
the
publication
for
gery
.
As
there
are
so
man
y
criterias
pro-
posed
by
indi
vidual
authors,
this
approach
benets
the
no
vice
to
look
upon
only
16
such
inuencial
highly
weighted
parameters
includes
Soliciting
Authors
for
publication
via
emails,
luring
authors
for
f
ast
publication
via
emails,
requesting
high/v
ery
lo
w
publication
char
ges
after
re
vie
w
,
short
re
vie
w
timing,
citef
actor
,
UIF
,
f
al-
sify
le
gitimate
impact
f
actors,
v
eried
impact
f
actors
are
Google,
Dimensions
and
W
eb
of
Science,
irre
gular
publication
frequenc
y
,
rapid
increase
in
the
publication
in
recent
year
,
claimed
open
access,
no
dened
Cop
y-
right
Polic
y/License,
parent
compan
y
information
hidden,
dead
links
on
journal/publisher
website,
presence
only
in
pre-print
serv
ers
and
disciplinary
repos,
authors
and
publishers
are
cross
countries,
lack
of
transparenc
y
in
editorial
board
de
v
elopment,
restricted
F
ocus
on
some
countries.
Additionally
this
has
been
observ
ed
that
the
majority
of
these
belongs
to
the
journal
le
v
el,
the
model
concluded
journal
le
v
el
is
the
highest
contrib
utor
to
academic
writing
for
geries.
Figure
3
sho
wcases
the
e
xclusi
v
e
dif
ferent
de
gree
analytics
present
between
these
parameters.
CriteriaChec
k
er:
a
knowledg
e
gr
aph
appr
oac
h
to
enhance
inte
grity
...
(Garima
Sharma)
Evaluation Warning : The document was created with Spire.PDF for Python.
980
❒
ISSN:
2502-4752
Figure
3.
De
gree
analytics
between
all
parameters
3.1.
Cor
e
entities
of
k
ey
parameters
of
intelligent
help
system
in
the
kno
wledge
graph
Further
,
e
v
aluating
the
centrality
analysis,
it
w
as
identied
that
the
‘journal-le
v
el
vulnerability’
is
the
most
central
f
actor
in
the
kno
wledge
graph
with
a
centrality
score
of
0.
8.
This
points
out
to
centrality
which
is
high
and
it
means
that
this
particular
product
plays
a
v
ery
important
role
within
the
netw
ork
of
predatory
journals.
Others
include
the
follo
wing
parameters;
‘soliciting
authors
’
being
0.
7,
‘high
publication
char
ges’
at
0.
65,
‘bogus
impact
f
actors’
at
0.
7,
and
‘f
alsied
editorial
board’
at
0.
55
as
presented
in
Figure
4.
The
f
act
that
these
parameters
are
so
highly
rated
points
to
their
importance
in
the
functioning
of
these
unscrupulous
journals.
Figure
4.
Centrality
of
k
e
y
parameters
in
obtained
kno
wledge
graph
3.2.
Le
v
eled
parameters
kno
wledge
graph
fr
om
multiple
pr
o
viders
The
le
v
eled
paramet
ers
kno
wledge
graph
as
sho
wn
in
abo
v
e
Figure
5
sho
wcase
the
identied
f
actors
and
their
interconnections,
pro
ving
that
predatory
journal
acti
vities
are
a
multif
aceted
issue.
The
comple
xity
of
these
netw
orks
is
signicant,
since
the
central
practices
include
man
y
f
actors
that
depend
on
each
other
.
This
mak
es
it
v
ery
hard
to
point
out
the
predatory
journals
and
therefore
there
is
the
need
for
a
multi-pronged
strate
gy
to
deal
with
this
issue.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
39,
No.
2,
August
2025:
973–986
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
981
Figure
5.
De
gree
of
centrality
for
inuential
parameters
3.3.
Effect
of
‘jour
nal-le
v
el
vulnerability’
on
the
structur
e
of
a
kno
wledge
graph
Based
on
the
analysis
of
Figure
4,
one
can
conclude
that
‘journal-le
v
el
vul
n
e
rability’
has
a
high
le
v
el
of
impact
f
actor
on
the
general
formation
of
the
kno
wledge
graph.
The
absence
of
this
parameter
af
fects
the
graph’
s
connecti
vity
and
its
topological
structure,
thereby
underlining
its
role
in
the
predatory
journal
en
vironment.
3.4.
Extended
description
of
the
most
signicant
v
ariables
and
their
interdependence
Thus,
Figure
6
is
an
analogous
representation
that
sho
ws
the
detailed
interactions
between
the
para
me-
ters,
thereby
elucidating
the
relations
within
the
kno
wledge
graph.
Such
specics
contrib
ute
to
the
vie
w
of
ho
w
precisely
relational
elements
lik
e
‘soliciting
authors’
and
‘high
publication
char
ges’
connect
and
contrib
ute
to
the
o
v
erall
netw
ork.
Figure
6.
Comparati
v
e
analysis
of
parameters
from
dif
ferent
criteria
pro
viders
3.5.
Comparing
centrality
scor
es
of
the
nodes
concer
ning
differ
ent
parameters
and
v
alidation
T
o
v
alidate
the
proposed
model
selected
16
inuential
criteria,
a
repository
of
retracted
papers
has
been
prepared
using
retract
ion
database
[6],
[7]
wherein
a
random
e
xtraction
of
50
retracted
paper
details
such
as
w
ork
title,
author
name,
journal
title,
publisher
title,
year
of
its
publication
and
year
of
retraction
is
done.
Further
,
the
criteria
pro
vided
by
dif
ferent
authors
is
check
ed
to
measure
the
accurac
y
of
predatory
identication
using
a
set
of
16
inue
n
t
ial
criteria
and
a
hundred-plus
a
v
ailable
criteria
pro
vided
by
dif
ferent
authors.
The
similarity
score
found
between
randomly
e
xtracted
lists
from
Bealls
and
randomly
e
xtracted
list
retraction
database
is
only
5%
stating
the
commonness
present
between
the
random
sampling
carried
out
between
the
tw
o
sets.
This
score
subsequently
increased
to
30%
between
Bealls
and
Shrestha’
s
proposed
w
ork.
CriteriaChec
k
er:
a
knowledg
e
gr
aph
appr
oac
h
to
enhance
inte
grity
...
(Garima
Sharma)
Evaluation Warning : The document was created with Spire.PDF for Python.
982
❒
ISSN:
2502-4752
Similarity
Score
=
Set
i
∩
Set
j
(3)
Where
i
and
j
are
tw
o
randomly
generated
sample
sets
from
a
dif
ferent
repository
.
Upon
analysis
thoroughly
with
acti
v
e
suspicious
journals,
a
set
of
ne
w
emer
ging
parameters
has
been
proposed
that
can
be
utilized
for
catching
the
fraudsters
at
the
three
dened
le
v
els
along
with
the
specied
inuential
parameters
set.
T
able
4
describes
t
he
ne
wly
identied
parameters
raising
a
bo
w
to
w
ards
suspicious
predatory
acti
vity
at
dif
ferent
le
v
els
e
xclusi
v
ely
seen
no
w
adays.
T
able
4.
Ne
wly
identied
parameters
for
intercepting
ne
w
era
predatory
S.
No.
Ne
w
age
predatory
criteria
Description
1
Sho
wcasing
b
usiness
address
of
de
v
eloped
countries,
major
editors
are
from
de
v
eloping
countries
The
of
cial
address
on
the
website
of
the
publisher
or
journal
is
claimed
to
be
from
a
de
v
eloped
country
wherein
all
the
editors
under
the
publisher
belong
to
de
v
eloping
countries.
2
Self-citations
The
self-citation
of
the
indi
vidual
author
,
journal,
publisher
,
society
,
and
institutions.
3
Evidence
of
multiple
publishers
in
a
single
journal
Single
journal
title
is
claimed
to
be
part
of
man
y
publishers.
4
Single
publisher
with
dif
ferent
numbers
of
journals
on
dif
ferent
websites
As
man
y
online
addresses
are
a
v
ailable
for
a
single
publisher
,
there
could
be
dif
ferent
numbers
of
journal
listings
at
dif
ferent
addresses.
5
Single
journal
title
with
tw
o
dif
ferent
ISSN
numbers
Dif
ferent
ISSNs
are
listed
at
dif
ferent
web
links.
6
Common
editor
among
all
the
journal-title
of
a
single
publisher
Single
editor
for
all
the
broad
areas
starting
from
medical
to
engineering
as
well
as
educational
and
social.
7
Journal
is
publishing
articles
without
ISSN
No
ISSN
w
as
present
e
v
en
after
the
publication
of
the
article
8
All
the
associated
journal
titles
are
accepting
ne
w
manuscripts
throughout
the
year
Besides
special
sessions,
the
journal
is
ready
to
accept
a
ne
w
manuscript
and
release
it
in
special
editions
with
higher
APCs.
9
Publisher
re
ady
to
pro
vide
membership
with-
out
author
af
liation
The
membership
is
generally
free
of
cost
and
can
be
tak
en
without
mentioning
an
y
af
liation
the
author
is
associated
with.
10
F
alsely
claiming
under
Scopus
after
e
xpiration
Journal
is
present
in
the
discontinued
list
of
Scopus
and
on
the
website,
it
is
claiming
to
be
Scopus.
11
No
editorial
board
is
listed
on
the
website
The
journal
or
publisher’
s
website
lacks
in
pro
viding
information
about
their
editors.
12
Dif
ferent
journal
name
on
the
publisher’
s
website
and
journal
website
The
journal
name
on
the
publisher’
s
website
is
dif
ferent
and
on
opening
its
web
link
the
name
and
details
are
dif
ferent.
T
o
measure
the
accurac
y
of
predatory
identication
by
studying
and
analyzing
dif
ferent
criteria
present
so
f
ar
,
a
ne
w
term
called
strength
score
has
been
coined
here.
A
higher
strengt
h
score
means
that
an
y
one
of
these
is
capable
enough
to
identify
the
maximum
of
the
predatory
present
in
either
list.
S
=
np
X
i
=0
a
(4)
a
=
p
i
∩
pr
i
(5)
Where,
S
=
strength
score,
n
=
a
positi
v
e
inte
ger
v
alue,
a
=
an
inte
ger
score
assigned
to
an
indi
vidual
parameter
,
np
=
number
of
identied
parameter
,
pr
=
parameter
title,
p
=
publisher
title,
i
=
a
positi
v
e
inte
ger
v
alue.
The
system
attains
a
nominal
strength
score
of
approx.
40%
of
these
randomly
prepared
lists
are
matched
with
16
inuential
parameters
so
e
xtracted.
The
o
v
erall
ef
cienc
y
of
the
system
increases
by
20%
if
we
incorporate
the
ne
wly
identied
parameters.
Consequently
,
incorporating
the
ne
wly
identied
parameter
with
inuential
paramete
rs
forms
an
ef
fecti
v
e
system
to
detect
predatory
journals.
Comparing
the
centrality
scores
of
all
the
parameters
is
presented
in
Figure
7,
b
ut
that
bar
chart
sho
ws
the
importance
of
each
parameter
clearly
.
Comparison
of
data
collected
on
v
arious
institutions
and
parameters
indicates
which
f
actors
ha
v
e
the
most
impact
and
hence
should
be
gi
v
en
undue
emphasis
while
trying
to
put
mechanisms
in
place
to
address
the
issue
of
predatory
journals.
The
present
study
e
xplored
a
comprehensi
v
e
approach
to
detect
the
16
inuencial
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
39,
No.
2,
August
2025:
973–986
Evaluation Warning : The document was created with Spire.PDF for Python.