I
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t
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na
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na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
,
p
p
.
73
~
82
I
SS
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DOI
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14
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1
.
p
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73
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S
e
a
rc
h
e
n
g
i
n
e
o
p
ti
m
iza
ti
o
n
(S
EO
)
is
an
imp
o
rta
n
t
i
n
tern
e
t
m
a
rk
e
ti
n
g
stra
teg
y
a
n
d
p
r
o
c
e
ss
th
a
t
fa
c
il
it
a
tes
m
a
x
imiz
in
g
an
in
te
n
d
e
d
we
b
site’s
v
isi
b
il
it
y
wit
h
se
a
rc
h
e
n
g
i
n
e
re
su
lt
s.
It
is
wid
e
ly
e
m
p
l
o
y
e
d
n
o
wa
d
a
y
s
to
im
p
ro
v
e
traffic
v
o
l
u
m
e
or
q
u
a
li
t
y
fr
o
m
se
a
rc
h
e
n
g
in
e
s
to
a
p
a
rti
c
u
lar
we
b
site.
Ev
e
n
t
h
o
u
g
h
a
sig
n
ifi
c
a
n
t
n
u
m
b
e
r
of
p
u
b
li
c
a
ti
o
n
s
imp
ly
th
e
e
ss
e
n
ti
a
l
a
sp
e
c
ts
of
S
EO,
o
n
l
y
a
fe
w
p
ro
v
id
e
g
e
n
e
ra
li
z
e
d
id
e
a
s
to
d
e
a
l
with
t
h
e
c
o
m
p
lex
str
u
c
tu
re
of
th
e
we
b
.
Also
,
th
e
c
rit
ica
l
issu
e
s
of
c
o
n
ten
t
q
u
a
li
ty
,
site
p
o
p
u
larity
,
k
e
y
wo
r
d
d
e
n
si
ty
,
a
n
d
p
u
b
li
c
it
y
fa
c
to
rs
we
re
n
o
t
m
u
c
h
c
o
n
si
d
e
re
d
in
t
h
e
trad
it
io
n
a
l
ra
n
k
i
n
g
a
lg
o
rit
h
m
s
d
u
rin
g
S
EO
p
ro
c
e
ss
e
s.
Th
is
h
a
s
n
e
g
a
ti
v
e
ly
in
fl
u
e
n
c
e
d
th
e
re
tri
e
v
a
l
ra
te
in
th
e
e
x
isti
n
g
S
EO
tec
h
n
i
q
u
e
s,
a
n
d
c
o
n
se
q
u
e
n
tl
y
,
i
n
a
d
e
q
u
a
te
se
a
rc
h
re
su
lt
s
we
re
o
b
tain
e
d
th
ro
u
g
h
se
a
r
c
h
e
n
g
in
e
s.
He
n
c
e
,
th
e
stu
d
y
c
o
n
s
id
e
rs
we
b
p
a
g
e
ra
n
k
in
g
as
a
th
e
o
re
ti
c
a
l
b
a
sis
fo
r
th
e
re
se
a
rc
h
a
n
d
a
d
d
re
ss
e
s
th
e
se
li
m
it
a
ti
o
n
s
in
th
e
e
x
isti
n
g
sy
ste
m
.
It
f
u
rth
e
r
imp
ro
v
e
s
S
EO
p
e
rfo
r
m
a
n
c
e
by
in
tro
d
u
c
i
n
g
a
u
n
iq
u
e
we
b
-
p
a
g
e
ra
n
k
in
g
stra
teg
ic
d
e
sig
n
to
g
a
i
n
h
i
g
h
e
r
p
a
g
e
ra
n
k
re
su
lt
s.
Th
e
re
su
lt
s
of
t
h
e
in
v
e
stig
a
ti
o
n
a
l
st
u
d
y
sh
o
w
th
a
t
th
e
p
ro
p
o
se
d
sy
ste
m
e
ffe
c
ti
v
e
ly
c
o
n
tri
b
u
tes
to
wa
rd
s
S
EO
wit
h
an
imp
ro
v
e
d
p
a
g
e
ra
n
k
i
n
g
stra
teg
y
a
n
d
a
ls
o
p
r
o
v
i
d
e
s
h
i
g
h
e
r
a
c
c
u
ra
c
y
in
c
a
lcu
latin
g
th
e
imp
o
rt
a
n
c
e
sc
o
re
of
we
b
p
a
g
e
s
wh
ich
is
c
o
m
p
a
ra
b
le
with
p
o
p
u
lar
ra
n
k
in
g
a
lg
o
rit
h
m
s
su
c
h
as
h
y
p
e
rl
in
k
-
in
d
u
c
e
d
to
p
ic se
a
rc
h
(
HITS
)
a
n
d
P
a
g
e
Ra
n
k
.
K
ey
w
o
r
d
s
:
Pag
eRan
k
Sear
ch
en
g
in
e
Sear
ch
en
g
in
e
o
p
tim
izatio
n
W
eb
p
ag
e
r
an
k
in
g
W
eb
p
ag
es
W
eb
s
tr
u
ctu
r
e
m
in
in
g
T
h
is
is
an
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Vin
u
th
a
My
s
o
r
e
Srin
iv
as
PET
R
esear
ch
C
en
tr
e,
Dep
ar
tm
en
t
of
C
o
m
p
u
ter
Scien
ce
an
d
E
n
g
in
ee
r
in
g
,
Un
iv
er
s
ity
of
My
s
o
r
e
Ma
n
d
y
a
,
I
n
d
ia
E
m
ail:
v
in
u
p
r
aj
2
0
1
4
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
co
n
ce
p
t
of
a
s
ea
r
ch
en
g
in
e
is
not
n
ew;
‘
Ar
ch
ie’
was
th
e
f
ir
s
t
to
be
r
elea
s
ed
in
th
e
ea
r
ly
1
9
9
0
s
,
s
p
ec
if
ically
to
s
ea
r
ch
f
ile
tr
an
s
f
er
p
r
o
to
c
o
l
(
FTP)
d
ata.
C
o
n
v
er
s
ely
,
‘
Ver
o
n
ica’
was
b
eliev
ed
to
be
th
e
f
ir
s
t
tex
t
-
b
ased
s
ea
r
ch
e
n
g
in
e
ev
er
cr
ea
ted
[
1
]
.
T
h
e
f
u
n
d
am
e
n
tal
m
o
ti
v
e
of
b
u
s
in
ess
es
in
th
e
cu
r
r
en
t
d
ig
ital
m
ar
k
etin
g
er
a
is
to
co
m
m
u
n
icate
ap
p
r
o
p
r
iate
in
f
o
r
m
atio
n
ab
o
u
t
th
eir
p
r
o
d
u
cts
an
d
s
er
v
ices
to
t
h
e
r
ig
h
t
cu
s
to
m
er
s
th
r
o
u
g
h
web
s
ites
with
m
in
im
al
ef
f
o
r
t,
wh
ich
h
as
led
to
an
in
cr
ea
s
e
in
th
e
n
u
m
b
er
of
web
s
ites
on
th
e
‘
wo
r
ld
wid
e
web
’
[
2
]
,
[
3
]
.
Sear
ch
en
g
in
e
th
e
o
r
y
d
ev
elo
p
s
f
r
o
m
t
h
e
p
er
s
p
ec
tiv
e
of
g
iv
i
n
g
a
p
p
r
o
p
r
iate
web
s
ite
p
ag
es
to
th
e
ta
r
g
eted
co
n
s
u
m
er
s
,
an
d
in
th
is
way
,
it
is
co
n
s
is
ten
t
with
th
e
f
u
n
d
am
en
tal
id
ea
s
of
in
ter
n
et
m
a
r
k
eti
n
g
.
Sear
ch
e
n
g
in
es
n
av
ig
ate
t
h
r
o
u
g
h
th
e
b
illi
o
n
s
of
p
a
g
es
av
ailab
le
on
th
e
in
ter
n
et
an
d
s
o
r
t
t
h
e
web
p
a
g
es
b
ase
d
on
th
ei
r
r
elev
an
ce
to
th
e
u
s
er
-
g
en
er
ated
q
u
e
r
y
(
e
.
g
.
,
k
e
y
wo
r
d
s
an
d
p
h
r
ases
)
[
4
]
–
[
6
]
.
C
u
r
r
e
n
tly
,
th
e
p
o
p
u
lar
in
te
r
n
et
s
ea
r
ch
en
g
in
es
ar
e
Go
o
g
le
,
Yah
o
o
,
a
n
d
B
in
g
.
As
a
r
esu
lt,
wh
e
n
ev
er
a
u
s
er
en
ter
s
a
s
p
ec
if
ic
p
h
r
ase
or
k
ey
wo
r
d
in
a
s
ea
r
c
h
in
s
tead
of
th
e
wh
o
le
we
b
s
ite
UR
L
f
o
r
a
c
o
m
p
an
y
,
th
e
s
ea
r
c
h
en
g
i
n
e
u
s
es
th
at
ter
m
to
f
in
d
th
e
ap
p
r
o
p
r
iate
web
p
ag
es
[
7
]
–
[
1
0
]
.
Fu
r
th
e
r
,
a
lis
t
with
th
e
m
o
s
t
p
er
tin
en
t
p
a
g
e
at
th
e
to
p
is
d
is
p
lay
ed
.
T
h
is
ap
p
r
o
ac
h
h
elp
s
o
r
g
an
izatio
n
s
r
ea
ch
th
ei
r
p
o
t
en
tial
co
n
s
u
m
er
s
by
ap
p
ea
r
i
n
g
at
th
e
to
p
of
th
e
s
ea
r
ch
r
e
s
u
lts
.
Ma
n
y
of
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
73
-
82
74
tr
ad
itio
n
al
s
ea
r
ch
en
g
in
e
o
p
tim
izatio
n
(
SEO
)
d
esig
n
s
tr
ateg
ies
ar
e
in
ten
d
e
d
to
attain
h
i
g
h
er
p
ag
e
r
an
k
r
esu
lts
wh
er
e
th
e
p
o
p
u
lar
ity
of
p
ag
e
r
an
k
in
g
al
g
o
r
ith
m
s
ar
is
es
[
1
1
]
,
[
1
2
]
.
Var
i
o
u
s
r
esear
ch
s
tu
d
ie
s
ar
e
b
ein
g
ca
r
r
ied
out
on
d
esig
n
in
g
ef
f
ec
tiv
e
SE
O
s
tr
ateg
ies
to
g
ain
h
ig
h
er
p
ag
e
r
an
k
r
esu
lts
f
r
o
m
th
e
p
er
s
p
ec
tiv
e
of
p
er
f
o
r
m
an
ce
im
p
r
o
v
em
e
n
t.
Ho
wev
er
,
s
ca
tte
r
ed
ch
allen
g
es
ar
is
e
wh
en
it
co
m
es
to
th
e
p
r
ac
tical
im
p
lem
e
n
tatio
n
of
r
esear
ch
-
b
ased
s
tu
d
ies
in
SEO.
T
h
e
r
es
ea
r
ch
-
b
ased
s
tu
d
ies
on
SEO
h
av
e
r
e
p
o
r
ted
v
ar
i
o
u
s
p
itfa
lls
a
s
s
o
ciate
d
with
th
e
ex
is
tin
g
r
an
k
in
g
alg
o
r
ith
m
s
[
1
3
]
.
Mo
s
t
ex
is
tin
g
p
a
g
e
r
an
k
al
g
o
r
ith
m
s
o
n
l
y
em
p
lo
y
a
f
ew
r
elev
an
t
k
ey
wo
r
d
s
to
r
etr
iev
e
to
p
-
k
web
p
a
g
es.
Als
o
,
th
e
r
esu
ltin
g
web
p
ag
es
m
a
y
not
m
ee
t
th
e
i
n
ten
d
ed
s
ea
r
c
h
q
u
er
y
.
Als
o
,
in
th
e
ex
is
tin
g
p
ag
e
r
an
k
alg
o
r
ith
m
s
,
th
e
n
ee
d
f
o
r
c
o
s
t
-
ef
f
ec
tiv
e
an
aly
tical
p
r
o
ce
s
s
in
g
with
r
eliab
le
an
d
h
ig
h
e
r
ef
f
icien
t
p
ag
e
r
an
k
in
g
is
h
ig
h
l
y
en
v
is
io
n
ed
to
en
s
u
r
e
th
e
ad
e
q
u
ate
p
er
f
o
r
m
a
n
ce
of
SEO
[
1
4
]
–
[
1
6
]
.
T
h
e
r
esear
ch
on
web
s
tr
u
ctu
r
e
m
in
in
g
ev
o
lv
es
with
th
e
p
u
r
p
o
s
e
of
d
is
co
v
er
in
g
in
f
o
r
m
atio
n
f
r
o
m
t
h
e
w
eb
.
It
also
s
ea
r
ch
es
f
o
r
in
f
o
r
m
atio
n
p
er
tain
in
g
to
r
elev
an
t
s
co
r
es
f
o
r
web
p
ag
es
an
d
h
y
p
er
lin
k
s
to
d
eter
m
in
e
th
e
q
u
ality
of
th
e
s
ea
r
c
h
r
esu
lts
.
It
b
asically
f
o
cu
s
es
on
o
r
g
an
i
zin
g
th
e
h
y
p
er
lin
k
s
tr
u
ctu
r
e
of
th
e
w
eb
.
T
h
e
s
tu
d
y
by
Du
b
ey
an
d
R
o
y
[
1
7
]
talk
s
a
bout
th
e
s
ig
n
if
ican
t
f
ac
to
r
s
of
p
ag
e
r
an
k
in
g
f
o
r
m
ea
s
u
r
i
n
g
th
e
im
p
o
r
tan
ce
an
d
b
eh
av
io
u
r
of
we
b
p
a
g
es.
J
ay
ar
am
an
et
a
l
.
[
1
8
]
also
h
ig
h
lig
h
t
s
th
at
th
e
p
er
f
o
r
m
an
ce
of
SEO
co
u
ld
be
in
cr
ea
s
e
d
if
th
e
p
r
o
ce
s
s
of
web
s
ite
r
an
k
i
n
g
is
d
esig
n
ed
in
an
o
p
tim
ize
d
f
lo
w
of
ex
ec
u
tio
n
f
o
r
p
ar
tic
u
lar
s
ea
r
ch
ter
m
s
in
s
ea
r
ch
en
g
in
es.
It
also
s
h
ar
es
th
e
co
n
v
en
tio
n
al
id
ea
s
r
elate
d
to
SEO
on
-
p
a
g
e
r
a
n
k
in
g
w
h
ile
m
an
ag
in
g
th
e
in
co
m
in
g
lin
k
s
an
d
web
s
ite
ch
ar
ac
ter
is
tic
f
ea
tu
r
es.
T
h
er
e
is
no
d
en
ial
of
th
e
f
ac
t
th
at
th
e
p
er
f
o
r
m
an
ce
of
tr
ad
itio
n
al
p
a
g
e
r
an
k
alg
o
r
ith
m
s
co
u
ld
be
im
p
r
o
v
e
d
by
u
s
in
g
web
m
i
n
in
g
tec
h
n
iq
u
es,
v
iz.
,
web
s
tr
u
ctu
r
e
,
web
co
n
ten
t
,
an
d
web
u
s
ag
e
as
h
ig
h
lig
h
ted
in
[
1
9
]
.
Alg
h
am
d
i
an
d
Alh
aid
ar
i
[
1
9
]
also
talk
ab
o
u
t
th
e
co
r
e
web
p
ag
e
r
an
k
in
g
alg
o
r
ith
m
s
an
d
e
x
p
lo
r
es
th
eir
id
ea
f
o
r
e
n
h
an
ci
n
g
th
e
p
er
f
o
r
m
an
ce
of
SEO.
T
h
e
id
ea
of
p
a
g
e
r
a
n
k
in
g
ev
o
lv
ed
f
r
o
m
th
e
m
o
s
t
p
o
p
u
lar
b
aselin
e
of
th
e
Pag
eRan
k
al
g
o
r
ith
m
,
wh
ich
Go
o
g
le
em
p
lo
y
ed
.
T
h
e
d
esig
n
a
n
d
o
p
e
r
atio
n
al
f
ac
to
r
s
of
t
h
e
tr
a
d
itio
n
al
Pag
eRan
k
alg
o
r
ith
m
u
tili
ze
th
e
web
s
tr
u
ctu
r
e
m
in
in
g
co
n
ce
p
t
to
co
m
p
u
te
th
e
p
a
g
e
r
an
k
v
alu
es.
Ho
wev
er
,
th
e
co
r
e
d
esig
n
m
o
d
el
of
th
is
alg
o
r
ith
m
co
m
p
u
tes
th
e
r
an
k
s
co
r
e
of
th
e
p
ag
e
at
in
d
ex
in
g
tim
e
an
d
ev
alu
ates
th
e
p
ag
e
s
co
r
e
co
n
s
id
er
in
g
th
e
in
-
l
in
k
s
,
wh
ich
c
o
u
ld
m
is
lead
th
e
s
ea
r
ch
r
esu
lts
in
th
e
p
o
s
t
-
r
a
n
k
in
g
p
h
ase,
as
claim
e
d
by
Su
r
i
et
a
l
.
[
3
]
.
T
h
e
co
m
p
u
tatio
n
al
ap
p
r
o
ac
h
of
r
ep
r
esen
t
in
g
th
e
wo
r
k
in
g
f
lo
w
of
h
y
p
e
r
tex
t
-
in
d
u
ce
d
to
p
ic
s
elec
tio
n
(
HI
T
S)
co
n
s
id
er
s
a
d
ir
ec
ted
g
r
a
p
h
s
tr
u
ct
u
r
e
wh
e
r
e
v
er
tices
r
ep
r
esen
t
th
e
web
p
ag
es
an
d
a
s
et
of
e
d
g
es
d
ep
icts
th
e
lin
k
s
[
20]
.
Z
h
an
g
et
a
l
.
[
2
0
]
claim
ed
th
at
th
e
p
e
r
f
o
r
m
a
n
ce
of
th
is
alg
o
r
ith
m
c
o
u
ld
be
im
p
r
o
v
ed
to
en
h
an
ce
th
e
s
co
p
e
of
SEO
o
p
e
r
atio
n
s
f
o
r
v
ar
io
u
s
u
s
er
q
u
e
r
ies.
C
h
o
wd
h
ar
y
a
n
d
Ku
m
ar
[
2
1
]
,
in
th
eir
s
tu
d
y
,
tal
k
ab
o
u
t
th
e
s
u
b
-
v
a
r
ian
t
of
th
e
m
a
in
Pag
eRan
k
alg
o
r
ith
m
,
wh
ich
is
r
ef
er
r
ed
to
as
th
e
weig
h
ted
p
ag
e
r
an
k
alg
o
r
it
h
m
(
W
PR
)
.
T
h
e
au
th
o
r
s
claim
th
at
th
is
b
aselin
e
ap
p
r
o
ac
h
h
as
a
b
r
o
ad
e
r
s
co
p
e
f
o
r
im
p
r
o
v
e
m
en
t
o
win
g
to
its
ad
v
an
tag
es
of
c
o
m
p
u
tin
g
b
o
th
in
-
lin
k
s
an
d
o
u
t
-
lin
k
s
to
war
d
s
m
ea
s
u
r
in
g
th
e
im
p
o
r
tan
ce
of
a
web
p
ag
e’
s
s
co
r
e.
Kelo
tr
a
et
a
l
.
[
2
2
]
d
esig
n
ed
an
im
p
r
o
v
e
d
m
eth
o
d
of
p
a
g
e
r
a
n
k
in
g
co
n
s
id
er
in
g
t
h
e
b
aselin
e
p
r
o
p
er
ties
of
th
e
tr
ad
itio
n
al
Pag
eRan
k
al
g
o
r
ith
m
.
Her
e
th
e
m
eth
o
d
o
p
er
ates
on
p
ag
e
r
an
k
b
ased
on
th
e
d
u
r
atio
n
a
u
s
er
s
p
en
d
s
on
th
e
web
p
a
g
e
an
d
it
s
lin
k
s
tr
u
ctu
r
e
.
T
h
e
au
th
o
r
s
claim
th
is
ap
p
r
o
ac
h
co
u
ld
ef
f
ec
t
iv
ely
o
f
f
er
a
b
etter
r
etr
iev
al
r
ate
f
o
r
web
s
ea
r
ch
e
n
g
in
es.
Hao
et
a
l
.
[
2
3
]
also
en
h
an
ce
d
th
e
t
r
ad
itio
n
al
Pag
eRa
n
k
alg
o
r
ith
m
f
o
r
web
co
n
ten
t
s
ea
r
ch
.
T
h
e
s
tu
d
y
by
Ush
a
an
d
Nag
ad
ee
p
a
[
2
4
]
also
in
tr
o
d
u
ce
s
a
h
y
b
r
id
p
ag
e
r
an
k
alg
o
r
ith
m
wh
e
r
e
th
e
alg
o
r
ith
m
u
tili
ze
s
web
s
tr
u
ctu
r
e,
web
co
n
ten
t
,
a
n
d
web
u
s
ag
e
m
in
in
g
tech
n
i
q
u
es
to
co
m
p
u
t
e
th
e
in
-
lin
k
s
of
th
e
web
p
ag
es.
T
u
teja
et
a
l
.
[
2
5
]
p
er
f
o
r
m
m
o
d
if
icatio
n
s
on
th
e
d
esig
n
f
ea
tu
r
es
of
tr
ad
itio
n
al
W
PR
co
n
s
id
er
in
g
th
e
f
r
eq
u
e
n
cy
of
v
is
its
of
in
-
lin
k
s
an
d
out
-
lin
k
s
,
wh
ich
is
f
u
r
th
er
co
m
b
in
ed
with
th
e
o
r
ig
in
al
m
ath
em
atica
l
f
o
r
m
u
latio
n
of
th
e
W
PR
alg
o
r
ith
m
s
tr
ateg
y
.
Sin
g
h
an
d
Sh
a
r
m
a
[
2
6
]
also
in
tr
o
d
u
ce
d
an
o
th
er
f
o
r
m
of
p
ag
e
r
a
n
k
alg
o
r
ith
m
,
wh
ich
co
n
s
id
er
s
b
o
th
web
s
tr
u
ctu
r
e
an
d
web
u
s
ag
e
m
in
in
g
tech
n
iq
u
es.
T
h
e
alg
o
r
ith
m
d
esig
n
co
m
p
u
tes
b
o
th
in
-
lin
k
s
an
d
o
u
t
-
lin
k
s
weig
h
ts
an
d
th
e
f
r
eq
u
e
n
cy
of
v
is
its
of
in
-
lin
k
s
on
web
p
ag
es.
T
h
e
s
tu
d
y
o
f
f
er
s
b
etter
r
esu
lts
with
its
s
t
r
ateg
y
f
o
r
im
p
r
o
v
in
g
s
ea
r
ch
e
n
g
in
e
r
esu
lts
.
An
o
th
er
ap
p
r
o
a
ch
to
p
ag
e
r
an
k
in
g
co
n
s
id
er
in
g
co
n
ten
t
weig
h
t
is
p
r
esen
ted
by
J
o
s
h
i
an
d
Gu
p
ta
[
2
7
]
;
h
er
e
,
th
e
s
tu
d
y
co
n
s
id
er
s
co
n
ten
t
weig
h
t
p
ar
am
eter
s
co
r
r
esp
o
n
d
in
g
to
web
p
ag
es
f
o
r
r
esp
ec
tiv
e
q
u
e
r
y
ter
m
s
to
ca
lcu
late
th
e
p
ag
e
r
an
k
s
.
In
th
e
s
tu
d
y
by
J
ag
an
ath
an
an
d
Desik
an
[
2
8
]
,
t
h
e
p
ag
e
r
an
k
alg
o
r
ith
m
also
co
m
p
u
tes
th
e
in
-
lin
k
an
d
o
u
t
-
lin
k
weig
h
ts
as
s
o
ciate
d
with
th
e
web
p
ag
es.
Ho
we
v
er
,
it
also
co
n
s
tr
u
cts
its
weig
h
t
m
atr
ix
,
wh
ich
f
ac
ilit
ates
r
etain
in
g
th
e
p
a
g
e
r
an
k
s
.
An
ag
en
t w
eig
h
ted
p
ag
e
r
an
k
i
n
g
alg
o
r
ith
m
(
AW
PR
)
is
d
esig
n
ed
as
an
en
h
a
n
ce
m
en
t
of
t
h
e
tr
ad
itio
n
al
W
P
R
alg
o
r
ith
m
,
w
h
ich
is
s
u
b
j
ec
ted
to
p
er
f
o
r
m
web
s
tr
u
ctu
r
e
m
in
in
g
wh
ile
co
m
p
u
tin
g
th
e
weig
h
t
of
in
-
lin
k
s
an
d
o
u
t
-
lin
k
s
p
r
esen
ted
by
Nag
ap
p
an
an
d
E
lan
g
o
[
2
9
]
.
An
o
th
er
s
im
ilar
s
tu
d
y
by
Gu
p
ta
an
d
Sin
g
h
[
3
0
]
also
r
ep
r
esen
ts
a
u
s
er
p
r
e
f
er
en
ce
-
b
ased
p
ag
e
r
an
k
i
n
g
al
g
o
r
ith
m
wh
er
e
it
ad
d
r
ess
es
th
e
to
p
ic
d
r
if
t
p
r
o
b
lem
of
t
h
e
AW
P
R
alg
o
r
ith
m
co
n
s
id
er
in
g
th
e
ad
v
an
tag
e
o
u
s
f
ac
to
r
s
of
web
-
u
s
ag
e
m
in
i
n
g
in
ca
lcu
lati
n
g
v
is
its
of
in
-
lin
k
.
Ma
h
ajan
et
al
.
[
3
1
]
in
tr
o
d
u
ce
d
a
ex
ten
d
e
d
weig
h
te
d
p
a
g
e
r
a
n
k
b
ased
o
n
v
is
its
o
f
lin
k
s
(
E
W
PR
VOL
)
alg
o
r
ith
m
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
of
W
PR
.
Alg
h
am
d
i
an
d
Alh
aid
ar
i
[
1
9
]
f
u
r
th
er
im
p
r
o
v
is
ed
th
e
p
er
f
o
r
m
a
n
ce
of
E
W
P
R
VOL
alg
o
r
ith
m
to
e
n
h
an
ce
s
ea
r
ch
en
g
in
e
p
er
f
o
r
m
an
ce
f
o
r
a
p
p
r
o
p
r
iate
r
etr
iev
al
of
s
ea
r
ch
r
esu
lts
co
n
s
id
er
in
g
web
s
tr
u
ctu
r
e,
w
eb
co
n
ten
t
an
d
web
u
s
ag
e
m
i
n
in
g
ap
p
r
o
ac
h
es.
Alh
aid
ar
i
et
a
l
.
[
3
2
]
h
av
e
also
in
tr
o
d
u
ce
d
a
d
ec
is
io
n
-
m
ak
i
n
g
tr
ial
an
d
ev
alu
atio
n
lab
o
r
ato
r
y
(
DE
MA
T
E
L
)
m
o
d
el
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
of
web
s
ites
to
war
d
s
f
u
lf
illi
n
g
th
e
u
s
er
’
s
r
eq
u
ir
em
en
ts
.
A
lin
ea
r
p
r
o
g
r
am
m
i
n
g
-
b
ased
s
tatis
t
ical
m
o
d
ellin
g
was
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
a
g
e
r
a
n
k
-
b
a
s
ed
a
n
a
lytica
l
d
esig
n
o
f
effec
tive
s
ea
r
ch
en
g
i
n
e
o
p
timiz
a
tio
n
…
(
V
in
u
th
a
M
yso
r
e
S
r
in
iva
s
)
75
in
tr
o
d
u
ce
d
to
e
n
h
an
ce
th
e
r
a
n
k
ed
lis
t
of
web
s
ea
r
ch
en
g
i
n
es
by
Am
in
an
d
E
m
r
o
u
z
n
ejad
[
3
3
]
.
T
h
e
ex
p
er
im
en
tal
o
u
tco
m
e
s
h
o
ws
th
at
o
p
tim
izin
g
th
e
s
ea
r
ch
e
n
g
in
e
r
esu
lts
tak
es
m
u
ch
lo
n
g
er
th
an
u
s
u
a
l.
An
o
th
er
r
a
n
k
in
g
tech
n
iq
u
e
is
d
esig
n
e
d
in
th
e
s
tu
d
y
of
B
o
zk
ir
a
n
d
Seze
r
[
3
4
]
,
wh
er
e
th
e
a
p
p
r
o
ac
h
co
n
s
id
er
s
c
o
m
p
u
tin
g
v
is
u
al
s
im
ilar
ities
am
o
n
g
web
p
ag
es.
Ho
wev
er
,
th
e
r
etr
iev
al
s
co
r
e
s
of
a
s
ea
r
ch
en
g
in
e
ar
e
af
f
ec
ted
by
h
ig
h
er
f
alse
p
o
s
itiv
e
s
co
r
es
[
3
4
]
.
A
s
im
ila
r
r
an
k
in
g
s
ch
em
e
is
also
in
tr
o
d
u
ce
d
Ah
m
ad
et
a
l
.,
[
3
5
]
,
w
h
er
e
an
en
u
m
er
ativ
e
f
ea
tu
r
e,
s
u
b
s
et
–
b
ased
r
a
n
k
in
g
,
m
o
d
ellin
g
was
d
ev
elo
p
e
d
to
im
p
r
o
v
e
th
e
s
ea
r
c
h
en
g
in
e
r
esu
lts
.
Ho
wev
er
,
th
e
r
etr
iev
al
r
ate
of
th
e
s
ch
em
e
was
f
o
u
n
d
to
be
v
er
y
p
o
o
r
.
T
h
er
e
ar
e
SEO
tech
n
iq
u
es
as
d
ep
ic
ted
in
th
e
s
tu
d
ies
in
[
3
6
]
,
[
3
7
]
,
wh
er
e
th
e
p
r
im
e
e
m
p
h
asis
was
laid
on
in
cr
ea
s
in
g
a
web
s
ite’
s
v
is
ib
ilit
y
.
Ho
we
v
er
,
th
e
tim
e
to
m
in
e
th
e
to
p
-
r
a
n
k
ed
web
s
ite
p
ag
es
was
not
m
in
im
ized
.
Als
o
,
th
e
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
is
m
u
ch
h
ig
h
er
in
th
e
ap
p
r
o
ac
h
of
[
3
7
]
.
Ozd
e
m
ir
ay
an
d
Altin
g
o
v
d
e
[
3
8
]
in
tr
o
d
u
c
ed
a
r
an
k
i
n
g
ag
g
r
e
g
atio
n
tech
n
iq
u
e
.
T
h
e
p
r
im
ar
y
m
o
tiv
e
of
th
is
s
tu
d
y
was
to
m
i
n
im
ize
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
of
web
p
ag
e
o
p
tim
izatio
n
s
.
H
o
wev
er
,
th
e
ex
p
er
im
en
tal
r
esu
lts
s
h
o
w
th
a
t
th
e
ap
p
r
o
ac
h
tak
es
c
o
m
p
ar
at
iv
ely
lo
n
g
er
to
r
etr
ie
v
e
th
e
to
p
-
r
an
k
ed
r
esu
lts
by
th
e
s
ea
r
ch
e
n
g
in
es.
B
an
ae
i
a
n
d
Ho
n
a
r
v
ar
[
1
4
]
h
a
v
e
e
n
co
u
r
a
g
ed
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
-
b
ased
ap
p
r
o
ac
h
es
in
SEO
f
o
r
d
eter
m
in
i
n
g
th
e
web
s
ite’
s
r
an
k
.
Ho
wev
er
,
th
e
ap
p
r
o
ac
h
was
f
o
u
n
d
s
atis
f
ac
to
r
y
with
th
e
test
d
ata
but
th
e
r
etr
iev
al
tim
e
f
o
r
ex
tr
ac
tin
g
to
p
-
k
web
p
a
g
e
r
esu
lts
was
h
ig
h
er
due
to
its
iter
ativ
e
o
p
er
atio
n
s
.
Ho
wev
er
,
th
e
c
h
allen
g
e
ar
is
es
to
b
ala
n
ce
th
e
tr
ad
e
-
o
f
f
b
et
wee
n
co
m
p
u
tin
g
ef
f
o
r
t
a
n
d
ef
f
icien
t
p
ag
e
r
an
k
in
g
f
o
r
r
etr
ie
v
al
of
tar
g
ete
d
p
ag
es
on
t
h
e
f
ir
s
t
p
a
g
e
of
s
e
ar
ch
r
esu
lts
.
T
h
e
an
aly
s
is
of
e
x
is
tin
g
ap
p
r
o
ac
h
es
to
SEO
ex
h
ib
its
th
at
th
er
e
h
av
e
b
ee
n
e
x
ten
s
iv
e
r
esear
ch
e
f
f
o
r
ts
to
war
d
s
im
p
r
o
v
in
g
t
h
e
an
al
y
tical
o
p
er
atio
n
s
in
p
ag
e
r
an
k
i
n
g
s
tr
ateg
ies.
Ho
wev
er
,
it
is
ess
en
tia
l
to
o
u
tlin
e
b
o
th
th
e
s
tr
en
g
th
f
ac
to
r
s
an
d
lim
itatio
n
s
in
th
e
ex
is
tin
g
d
esig
n
s
of
p
ag
e
r
a
n
k
s
tr
ateg
ies
so
th
at
r
ea
d
er
s
will
h
av
e
a
clea
r
er
id
ea
ab
o
u
t
th
e
s
c
o
p
e
of
im
p
r
o
v
em
en
t
f
o
r
th
e
f
u
tu
r
e
lin
e
of
r
esear
c
h
.
T
h
e
an
aly
s
is
of
th
e
co
n
v
e
n
tio
n
al
s
tr
ateg
ies
f
o
r
SEO
-
b
ased
p
ag
e
r
an
k
in
g
alg
o
r
ith
m
s
g
en
e
r
ates
two
g
en
e
r
al
r
esear
ch
q
u
esti
o
n
s
(
R
Qs),
wh
ich
ar
e
as
f
o
llo
ws:
‒
R
Q1
:
W
h
at
co
n
tr
ib
u
tes
to
s
ea
r
ch
en
g
in
e
r
an
k
i
n
g
s
?
‒
R
Q2
:
W
h
at
can
web
c
o
n
ten
t
cr
ea
to
r
s
an
d
a
d
m
in
s
do
to
m
ak
e
th
eir
co
n
te
n
t
an
d
s
ites
ea
s
ier
to
f
in
d
by
au
d
ien
ce
s
u
s
in
g
s
ea
r
ch
en
g
in
e
s
?
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
also
r
e
v
iewe
d
s
o
m
e
ess
en
tial
p
ag
e
r
an
k
in
g
s
tr
ateg
ies
in
SEO
o
p
er
atio
n
s
.
Ho
wev
er
,
d
esp
ite
h
av
in
g
,
p
o
p
u
lar
ity
,
th
e
e
x
is
tin
g
p
a
g
e
r
an
k
i
n
g
d
esig
n
s
in
SEO
s
u
f
f
er
s
f
r
o
m
v
ar
io
u
s
s
h
o
r
tco
m
in
g
s
,
wh
ic
h
co
u
l
d
be
n
o
ted
as
f
o
llo
ws:
‒
Mo
s
t
ex
is
ti
n
g
p
ag
e
r
an
k
i
n
g
s
t
r
ate
g
i
es
do
n
o
t
co
v
er
m
u
lti
p
l
e
p
a
r
a
m
e
te
r
s
d
u
r
i
n
g
S
E
O
o
p
er
at
io
n
s
.
T
h
is
le
ad
s
to
cr
iti
ca
l
is
s
u
es
r
e
g
a
r
d
i
n
g
c
o
n
ten
t
q
u
ali
ty
,
s
i
te
p
o
p
u
la
r
it
y
,
k
e
y
w
o
r
d
d
en
s
it
y
,
a
n
d
p
u
b
li
cit
y
f
ac
t
o
r
s
[
1
7
]
–
[
2
1
]
.
‒
E
v
en
th
o
u
g
h
p
ag
e
r
an
k
in
g
an
aly
tical
d
esig
n
m
o
d
elin
g
s
ar
e
h
ig
h
ly
en
co
u
r
ag
ed
f
o
r
e
f
f
ec
tiv
e
s
ea
r
ch
e
n
g
in
e
r
esu
lts
to
war
d
s
r
etr
iev
in
g
to
p
-
k
tar
g
eted
p
ag
es,
t
h
e
r
etr
ie
v
al
r
ate
in
m
o
s
t
of
th
e
ex
is
tin
g
SEO
ap
p
r
o
ac
h
es
co
u
ld
be
b
etter
.
‒
T
h
e
ex
is
tin
g
p
ag
e
r
a
n
k
alg
o
r
ith
m
s
f
o
r
SEO
n
ee
d
to
m
ee
t
th
e
r
eq
u
ir
em
e
n
ts
f
o
r
in
ten
d
e
d
s
ea
r
ch
as
it
ev
alu
ates
th
e
p
ag
e
s
co
r
e
by
c
o
n
s
id
er
in
g
o
n
ly
lin
k
s
.
Als
o
,
m
o
s
t
of
th
e
ex
is
tin
g
SEO
d
esig
n
s
ar
e
af
f
ec
ted
by
th
e
p
r
o
b
lem
of
to
p
ic
d
r
if
t
[
2
2
]
,
[
2
4
]
.
‒
In
m
a
n
y
c
a
s
e
s
,
c
h
a
l
l
e
n
g
es
a
r
is
e
w
h
e
n
d
e
a
l
i
n
g
w
i
t
h
l
i
n
k
s
c
o
n
n
ec
t
e
d
w
i
t
h
t
w
o
or
more
s
i
m
il
a
r
s
i
t
e
s
.
In
c
o
n
t
r
a
s
t
,
s
o
m
e
l
i
n
k
s
c
o
u
l
d
be
c
r
e
a
te
d
u
n
r
e
a
l
i
s
t
i
c
al
l
y
to
e
n
c
o
u
r
a
g
e
t
h
e
ap
p
e
a
r
a
n
c
e
of
s
p
a
m
p
a
g
e
s
by
t
h
e
s
e
a
r
c
h
e
n
g
i
n
e
(
SE
)
to
be
in
t
h
e
t
o
p
-
r
a
n
k
e
d
s
ea
r
c
h
r
e
s
u
l
ts
.
T
h
is
a
ls
o
m
i
s
l
e
a
d
s
t
h
e
r
e
s
u
l
ts
to
t
h
e
u
s
e
r
s
[
2
9
]
,
[
3
0
]
.
‒
It
is
also
o
b
s
er
v
ed
th
at
th
e
tr
ad
i
tio
n
al
SEO
a
p
p
r
o
ac
h
es
ex
ec
u
tio
n
w
o
r
k
f
l
o
w
m
o
d
els
ar
e
co
m
p
u
tatio
n
ally
co
m
p
lex
a
n
d
af
f
ec
ts
th
e
r
etr
ie
v
al
tim
e
p
er
f
o
r
m
a
n
ce
,
ev
e
n
th
o
u
g
h
,
in
m
a
n
y
ca
s
es,
in
ad
e
q
u
a
te
SE
r
esu
lts
ar
e
also
o
b
tain
ed
[
3
5
]
.
‒
T
h
e
e
x
i
s
t
i
n
g
S
E
O
a
p
p
r
o
a
c
h
e
s
n
e
e
d
to
e
n
s
u
r
e
a
p
r
o
p
e
r
b
a
l
a
n
c
e
b
e
t
w
e
e
n
t
h
e
r
e
t
r
i
e
v
a
l
r
a
t
e
of
t
h
e
t
o
p
-
r
a
n
k
e
d
s
e
a
r
c
h
r
e
s
u
l
t
s
w
i
t
h
t
h
e
c
o
m
p
u
t
a
t
i
o
n
a
l
c
o
m
p
l
e
x
i
t
y
a
s
p
e
c
t
s
w
h
i
c
h
i
n
f
l
u
e
n
c
e
s
t
h
e
t
i
m
e
of
r
e
t
r
i
e
v
a
l
[
1
4
]
,
[
3
6
]
–
[
3
8
]
.
All
th
e
r
esear
ch
m
e
n
tio
n
ed
a
b
o
v
e
p
r
o
b
lem
s
ar
e
id
e
n
tifie
d
to
h
av
e
co
n
cr
ete
s
o
lu
tio
n
s
;
h
en
ce
,
th
e
p
r
o
p
o
s
ed
s
ch
em
e
d
ep
lo
y
s
a
n
o
v
el
p
ag
e
r
an
k
s
tr
ateg
y
to
ad
d
r
ess
th
ese
o
p
en
-
en
d
r
esear
ch
p
r
o
b
lem
s
in
SEO
.
T
h
e
s
tu
d
y
ad
d
r
ess
es
th
e
p
r
ac
tical
im
p
lem
en
tatio
n
co
n
s
tr
ain
ts
ass
o
ciate
d
with
SEO
f
r
o
m
th
e
co
s
t
p
o
in
t
of
v
iew
an
d
aim
s
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
of
SEO
with
an
o
p
tim
ized
p
ag
e
r
a
n
k
s
tr
ateg
y
.
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
,
in
th
is
r
eg
ar
d
,
in
tr
o
d
u
c
es
a
f
r
am
ewo
r
k
th
at
an
aly
tical
ly
d
ev
elo
p
s
an
o
p
tim
ized
p
ag
e
r
an
k
alg
o
r
ith
m
to
o
b
tain
th
e
s
ig
n
if
ican
t
r
an
k
in
g
of
we
b
p
a
g
es
so
th
at
i
n
ten
d
ed
s
ea
r
ch
r
esu
lts
co
u
ld
be
p
u
b
lis
h
ed
with
less
er
co
m
p
u
tatio
n
al
ef
f
o
r
t.
T
h
e
p
r
o
p
o
s
ed
s
tr
ateg
y
of
p
a
g
e
r
an
k
i
n
g
is
m
o
d
eled
to
f
ac
ilit
ate
g
ain
in
g
h
ig
h
e
r
p
ag
e
r
a
n
k
r
esu
lts
with
an
o
p
tim
ized
f
l
o
w
of
ex
ec
u
tio
n
an
d
also
en
h
an
ce
s
th
e
r
etr
iev
al
p
e
r
f
o
r
m
an
ce
wh
ile
co
v
er
in
g
m
u
ltip
le
p
ar
am
eter
s
.
T
h
e
u
n
iq
u
en
ess
of
th
e
p
r
o
p
o
s
ed
SEO
a
p
p
r
o
ac
h
lies
in
th
e
f
ac
t
th
at
it
in
co
r
p
o
r
ates
n
o
v
el
an
aly
tical
s
tr
ateg
ic
ex
ec
u
tio
n
to
co
n
tr
ib
u
te
to
war
d
s
b
est
s
ea
r
ch
r
esu
lts
th
r
o
u
g
h
im
p
r
o
v
in
g
th
e
p
er
f
o
r
m
an
ce
of
th
e
p
ag
e
r
a
n
k
alg
o
r
ith
m
a
n
d
also
en
s
u
r
in
g
ad
e
q
u
ate
co
n
v
e
r
g
en
ce
v
al
u
e
f
o
r
r
etr
iev
al
of
t
o
p
-
r
an
k
ed
tar
g
eted
p
ag
es
with
in
co
n
s
id
er
a
b
le
iter
atio
n
s
.
T
h
e
en
tire
m
an
u
s
cr
ip
t
is
s
tr
u
c
tu
r
ed
as
f
o
ll
o
ws
.
In
s
ec
tio
n
2
,
th
e
ex
ten
s
iv
e
id
ea
co
r
r
esp
o
n
d
i
n
g
to
t
h
e
r
esear
ch
m
eth
o
d
o
lo
g
y
is
d
is
cu
s
s
ed
,
alo
n
g
with
elab
o
r
ated
d
i
s
cu
s
s
io
n
on
th
e
s
y
s
tem
d
esig
n
,
an
d
th
e
al
g
o
r
ith
m
d
escr
ip
tio
n
.
Sectio
n
3
h
ig
h
lig
h
ts
th
e
ac
q
u
ir
e
d
r
esu
lts
an
d
ju
s
tifie
s
th
e
p
r
o
p
o
s
ed
s
tu
d
y
'
s
ef
f
ec
tiv
en
ess
.
Fin
ally
,
s
ec
tio
n
4
also
p
r
o
v
id
es
co
n
cl
u
s
iv
e
r
em
ar
k
s
ab
o
u
t
th
e
o
v
er
all
wo
r
k
an
d
h
ig
h
lig
h
ts
its
n
o
v
el
co
n
tr
ib
u
tio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
73
-
82
76
2.
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
in
tr
o
d
u
ce
s
a
s
im
p
lifie
d
an
d
u
n
iq
u
e
d
esig
n
ap
p
r
o
ac
h
to
f
o
r
m
u
late
a
p
ag
e
r
an
k
s
tr
ateg
y
f
o
r
ef
f
ec
tiv
e
s
ea
r
ch
e
n
g
in
e
o
p
er
atio
n
s
.
It
ex
p
lo
r
es
t
h
e
id
ea
s
b
eh
in
d
t
h
e
b
aselin
e
Pag
eRan
k
m
o
d
els
an
d
f
u
r
th
er
attem
p
ts
to
o
p
tim
ize
its
f
lo
w
of
ex
ec
u
tio
n
to
n
o
r
m
alize
r
an
k
in
g
a
s
et
of
web
s
ites
wh
ich
also
co
n
tr
ib
u
tes
to
war
d
s
ef
f
ec
tiv
e
s
ea
r
ch
en
g
in
e
r
esu
lts
.
Her
e
th
e
p
r
im
e
m
o
ti
v
e
of
th
is
al
g
o
r
ith
m
is
to
m
ak
e
th
e
ta
r
g
eted
s
ea
r
ch
r
esu
lts
ap
p
ea
r
on
th
e
to
p
of
th
e
f
r
o
n
t
p
ag
e
with
th
e
p
ag
es
h
a
v
in
g
th
e
h
ig
h
est
r
ele
v
an
cy
s
co
r
e
s
.
T
h
e
co
n
v
en
tio
n
al
SEO
d
esig
n
s
ar
e
b
ased
on
th
e
Pag
eRan
k
alg
o
r
ith
m
v
ar
ian
ts
,
wh
ich
aim
to
r
etain
h
ig
h
p
ag
e
r
an
k
s
co
r
es
at
th
e
to
p
of
t
h
e
f
r
o
n
t
p
a
g
e
co
n
s
id
er
i
n
g
a
s
p
ec
if
ic
u
s
er
’
s
p
r
o
v
i
d
ed
s
ea
r
ch
(
q
u
er
y
)
.
T
h
e
s
tu
d
y
ap
p
lies
a
p
o
wer
m
eth
o
d
th
at
u
p
d
ates
th
e
weig
h
ted
r
ef
er
en
ce
co
u
n
ts
g
e
n
er
ated
by
th
e
h
y
p
er
lin
k
s
b
etwe
en
p
ag
es.
Fu
r
th
er
,
th
e
s
y
s
tem
ap
p
lies
a
co
n
n
ec
tiv
ity
v
ec
to
r
an
d
ev
al
u
ates
th
e
h
y
p
er
lin
k
s
b
etwe
en
th
e
p
ag
es
to
m
ea
s
u
r
e
th
e
in
-
d
eg
r
ee
a
n
d
out
-
d
e
g
r
ee
f
o
r
t
h
e
r
esp
ec
tiv
e
p
a
g
es.
T
h
e
co
m
p
u
tatio
n
f
u
r
th
er
also
ex
p
lo
r
es
th
e
p
r
o
b
ab
ilis
tic
f
ac
to
r
s
of
u
s
er
b
eh
a
v
io
u
r
a
n
d
c
o
n
s
tr
u
cts
a
tr
an
s
itio
n
al
p
r
o
b
ab
ilis
tic
m
atr
ix
.
Fu
r
th
er
,
th
e
s
tr
ateg
y
ap
p
lies
Per
r
o
n
–
Fr
o
b
en
i
u
s
th
eo
r
em
an
d
ass
es
s
es
th
e
s
ca
lin
g
f
ac
to
r
to
o
b
tain
th
e
p
ag
e
r
an
k
s
co
r
e
f
o
r
r
esp
ec
tiv
e
p
ag
es
.
T
h
e
s
tu
d
y
also
ap
p
lies
a
n
o
r
m
ali
za
tio
n
tech
n
iq
u
e
to
m
ak
e
th
e
r
an
k
in
g
of
th
e
web
p
ag
es
more
r
eli
ab
le
with
in
co
n
s
id
er
ab
le
am
o
u
n
t
of
iter
atio
n
f
o
r
ex
ec
u
tio
n
s
ch
em
a.
T
h
e
p
ag
e
s
co
r
es
ar
e
f
u
r
th
er
n
o
r
m
alize
d
co
n
s
id
er
in
g
th
e
to
t
al
co
u
n
t
of
out
-
g
o
in
g
lin
k
s
of
th
e
s
o
u
r
ce
n
o
d
es.
T
h
e
s
tr
ateg
y
also
s
h
ar
es
th
e
id
ea
of
n
o
r
m
alizin
g
p
ag
e
r
a
n
k
of
e
ac
h
p
ag
e
co
n
s
id
er
in
g
a
m
ea
n
v
alu
e
o
p
er
atio
n
an
d
f
u
r
th
er
ass
ess
e
s
th
e
iter
atio
n
s
to
r
etain
h
ig
h
est
p
ag
e
r
a
n
k
s
co
r
es
f
o
r
m
o
r
e
s
ig
n
if
ican
t
p
ag
es
d
u
r
in
g
SEO
o
p
er
atio
n
s
.
T
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
’
s
p
r
im
e
m
o
tiv
e
is
to
d
esig
n
a
n
d
d
e
v
e
lo
p
a
n
o
v
el
an
aly
tical
o
p
er
atio
n
f
o
r
p
ag
e
r
an
k
in
g
in
SEO.
An
e
x
p
licit
s
y
s
tem
d
esig
n
a
n
d
m
o
d
elin
g
ap
p
r
o
ac
h
is
co
n
s
tr
u
cte
d
to
r
ea
lize
th
e
s
y
s
tem
o
p
er
atio
n
s
f
o
r
ef
f
ec
tiv
e
s
ea
r
ch
en
g
in
e
r
esu
lts
f
r
o
m
b
o
t
h
r
etr
ie
v
al
p
er
f
o
r
m
an
ce
a
n
d
co
s
t
of
co
m
p
u
tatio
n
p
o
in
t
of
v
iew.
T
h
e
s
y
s
tem
d
esig
n
m
o
d
elin
g
co
n
s
id
er
s
g
r
a
p
h
th
eo
r
y
to
ex
p
lo
r
e
th
e
lin
k
s
tr
u
ctu
r
e
of
wo
r
ld
wid
e
web
(
www)
.
Her
e
th
e
s
y
s
tem
d
es
i
g
n
an
d
m
o
d
elin
g
ar
e
also
in
s
p
ir
ed
f
r
o
m
th
e
co
n
v
en
tio
n
al
Pag
eRan
k
alg
o
r
ith
m
to
war
d
s
r
an
k
in
g
th
e
s
ea
r
ch
e
n
g
in
e
r
esu
lts
.
T
h
e
s
tu
d
y
also
ex
p
lo
r
es
th
e
id
ea
t
h
r
o
u
g
h
wh
ich
Pag
eRan
k
alg
o
r
ith
m
r
an
k
s
a
co
llectio
n
of
web
s
ites
.
2
.
1
.
M
o
dellin
g
of
m
a
rko
v
pro
ce
s
s
T
h
e
s
tu
d
y
co
n
s
id
er
s
t
h
e
th
e
o
r
y
of
th
e
Ma
r
k
o
v
ch
ai
n
or
Ma
r
k
o
v
p
r
o
ce
s
s
[
3
9
]
to
d
esig
n
th
e
an
aly
tical
o
p
er
atio
n
s
of
p
a
g
e
r
an
k
in
g
in
SEO.
T
h
e
u
n
d
er
ly
in
g
id
ea
of
Ma
r
k
o
v
ch
ai
n
r
ef
er
s
to
a
s
to
ch
asti
c
p
r
o
ce
s
s
in
wh
ich
s
tates
ch
an
g
e
f
o
r
t
he
tr
a
n
s
itio
n
p
r
o
b
ab
ilit
ies.
Her
e
t
h
e
tr
a
n
s
itio
n
p
r
o
b
a
b
ilit
ies
ar
e
d
eter
m
in
e
d
by
th
e
s
tep
s
of
th
e
p
r
ev
io
u
s
tim
e
s
tep
.
T
h
is
th
eo
r
y
is
cr
u
cial
to
m
o
d
el
la
r
g
e
-
s
ca
le
s
y
s
tem
s
with
r
an
d
o
m
b
eh
a
v
io
u
r
wh
er
e
t
h
e
ar
ea
of
s
ea
r
ch
en
g
in
e
o
p
er
atio
n
als
o
ar
is
es.
In
web
s
u
r
f
in
g
,
a
u
s
er
n
av
ig
ates
f
r
o
m
o
n
e
p
ag
e
to
a
n
o
th
er
by
r
a
n
d
o
m
l
y
ch
o
o
s
in
g
th
e
o
u
tg
o
in
g
lin
k
s
.
T
h
is
can
lead
to
th
e
d
ea
d
en
d
of
web
p
ag
es
with
no
o
u
tg
o
in
g
lin
k
s
.
Alter
n
ativ
ely
,
it
can
also
h
ap
p
e
n
th
at
th
e
u
s
er
cy
cles
ar
o
u
n
d
in
ter
c
o
n
n
ec
ted
p
ag
es.
So
,
it
is
ev
id
e
n
t
th
at
a
u
s
er
ten
d
s
to
ch
o
o
s
e
a
r
an
d
o
m
p
ag
e
f
r
o
m
t
h
e
web
in
a
ce
r
tain
f
r
ac
tio
n
of
tim
e.
T
h
is
s
ce
n
ar
io
is
o
f
ten
ca
lled
a
r
an
d
o
m
walk
an
d
th
eo
r
etica
lly
can
be
d
escr
ib
ed
with
Ma
r
k
o
v
p
r
o
ce
s
s
.
2
.
2
.
Ana
ly
t
ica
l
o
pera
t
io
n
of
pa
g
e
ra
nk
s
t
ra
t
e
g
y
A
p
r
o
b
ab
ilis
tic
s
tr
ateg
ic
ev
alu
atio
n
is
r
elate
d
to
esti
m
atin
g
p
ag
e
r
an
k
s
co
r
e
.
Her
e,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
co
n
s
id
er
s
a
lim
ited
p
r
o
b
ab
ilis
tic
s
co
r
e
(
)
,
wh
ich
d
en
o
tes
th
e
lik
elih
o
o
d
th
at
a
r
a
n
d
o
m
web
s
u
r
f
er
will
v
is
it
an
y
web
s
ite.
It
also
g
o
es
by
t
h
e
n
am
e
Pa
g
eRan
k
.
L
et
W
is
a
s
et
of
web
p
ag
es
wh
ich
can
be
r
ep
r
esen
ted
as
=
{
}
=
1
.
Her
e
r
e
p
r
esen
ts
th
e
n
u
m
b
er
of
web
p
ag
es.
T
h
e
f
o
r
m
u
lated
p
a
g
e
r
a
n
k
in
g
s
tr
ateg
y
b
asic
ally
o
p
er
ates
on
t
h
e
co
u
n
t
of
i
n
co
m
in
g
(
)
an
d
o
u
t
g
o
in
g
lin
k
s
(
)
to
a
p
ag
e.
T
h
e
s
tr
ateg
y
also
ev
alu
a
tes
th
e
q
u
ality
of
lin
k
s
to
a
p
ag
e
f
o
r
ef
f
ec
tiv
e
SEO.
T
h
is
id
ea
h
elp
s
d
eter
m
in
in
g
an
d
g
e
n
er
al
izin
g
th
e
d
eg
r
ee
of
im
p
o
r
tan
ce
(
DOI
)
of
a
p
ar
ticu
l
ar
web
s
ite.
Fo
r
ex
am
p
le,
a
g
r
a
p
h
-
b
ased
r
ep
r
esen
tatio
n
ca
n
be
m
o
d
eled
to
d
ep
ic
t
th
r
ee
web
p
a
g
es
s
u
ch
as
=
{
}
=
1
3
in
th
e
f
o
r
m
of
th
r
ee
v
er
tices.
T
h
e
web
p
ag
es
can
be
r
ea
ch
ed
th
r
o
u
g
h
th
e
f
o
r
m
u
latio
n
of
h
y
p
e
r
lin
k
s
(
ℎ
)
wh
ich
b
eg
in
s
at
an
y
of
t
h
e
r
o
o
t
p
ag
e.
T
h
e
p
r
o
p
o
s
ed
r
a
n
k
in
g
s
tr
ateg
y
in
itially
co
n
s
tr
u
cts
th
e
co
n
n
ec
tiv
ity
v
e
cto
r
an
d
f
u
r
th
e
r
en
ab
les
an
e
x
p
licit
f
u
n
ctio
n
al
m
o
d
u
le
ƒ
1
(
)
to
co
m
p
u
te
th
e
r
o
w
an
d
co
lu
m
n
s
u
m
m
atio
n
f
r
o
m
th
e
×
.
T
h
e
s
y
s
tem
also
co
m
p
u
t
es
a
p
er
ce
iv
ed
im
p
o
r
tan
ce
f
ac
to
r
(
)
f
o
r
a
p
ar
ticu
lar
web
s
ite
or
web
-
p
ag
e
th
r
o
u
g
h
th
e
p
r
o
p
o
s
ed
p
ag
e
r
an
k
s
tr
ateg
y
f
o
r
ef
f
ec
tiv
e
SEO
.
T
h
e
p
r
o
p
o
s
ed
p
a
g
e
r
an
k
s
tr
ateg
y
is
im
p
lem
en
te
d
o
v
er
SEO
f
o
r
e
f
f
ec
tiv
e
s
ea
r
c
h
en
g
in
e
r
esu
lts
co
n
s
id
er
in
g
g
r
a
p
h
-
b
ased
m
o
d
elin
g
.
T
h
e
co
n
n
ec
tiv
ity
v
ec
to
r
r
ep
r
esen
ts
co
n
n
ec
tio
n
or
h
y
p
e
r
lin
k
b
etwe
en
p
ag
e
to
p
ag
e
.
Als
o
,
th
e
s
tr
ateg
y
f
u
r
th
er
esti
m
ates
a
p
r
o
b
a
b
ilit
y
f
ac
to
r
of
,
wh
ich
im
p
lies
t
h
e
p
o
s
s
ib
ilit
y
of
an
I
n
ter
n
et
u
s
er
to
r
an
d
o
m
ly
s
elec
t
an
d
f
o
llo
ws
a
lin
k
of
a
c
u
r
r
e
n
t
p
ag
e.
An
o
th
e
r
p
r
o
b
ab
ilit
y
m
ea
s
u
r
e
of
im
p
lies
th
e
p
o
s
s
ib
il
ity
of
ch
o
o
s
in
g
a
s
p
ec
if
ic
r
an
d
o
m
p
ag
e
w
h
ich
c
an
be
co
m
p
u
ted
as
(
1
)
:
δ
=
(
1
−
P
r
)
n
⁄
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
a
g
e
r
a
n
k
-
b
a
s
ed
a
n
a
lytica
l
d
esig
n
o
f
effec
tive
s
ea
r
ch
en
g
i
n
e
o
p
timiz
a
tio
n
…
(
V
in
u
th
a
M
yso
r
e
S
r
in
iva
s
)
77
Her
e
th
e
×
r
ep
r
esen
ts
a
c
o
n
n
ec
t
iv
ity
m
atr
ix
w
h
ich
co
r
r
esp
o
n
d
s
to
a
p
o
r
tio
n
of
web
s
tr
u
ctu
r
e
.
T
h
e
p
r
o
p
o
s
ed
f
o
r
m
u
latio
n
of
p
ag
e
r
an
k
s
tr
ateg
y
also
esti
m
ates
th
e
q
u
a
n
titi
es
of
an
d
wh
ich
i
n
d
icate
s
th
e
in
-
d
eg
r
ee
an
d
out
-
d
e
g
r
ee
m
ea
s
u
r
e
of
ℎ
p
ag
e.
T
h
e
s
tr
ateg
ic
s
o
lu
tio
n
f
u
r
th
er
also
c
o
n
s
tr
u
cts
a
m
atr
ix
wh
ich
is
also
of
th
e
d
im
en
s
io
n
(
×
).
T
h
e
elem
en
ts
of
can
be
r
e
p
r
esen
t
ed
with
th
e
(
2
)
.
T
ij
=
{
P
r
V
ij
co
l
j
+
δ
∶
c
ol
j
≠
0
1
n
∶
c
ol
j
=
0
(
2
)
Her
e
th
e
co
m
p
u
tatio
n
of
tak
es
p
lace
by
s
ca
lin
g
th
e
co
n
n
e
ctiv
ity
m
atr
ix
with
r
esp
ec
t
to
its
co
lu
m
n
s
u
m
s
.
Her
e
th
e
ℎ
co
lu
m
n
in
in
d
icate
s
th
e
p
o
s
s
ib
ilit
y
of
an
u
s
er
ju
m
p
in
g
f
r
o
m
o
n
e
p
a
g
e
to
a
n
o
th
er
p
ag
es
in
th
e
web
.
If
it
is
f
o
u
n
d
th
at
th
e
ℎ
p
ag
e
is
d
ea
d
en
d
th
en
it
h
as
n
o
t
o
u
t
-
g
o
i
n
g
lin
k
s
to
be
ass
o
ciate
d
.
T
h
e
s
tr
ateg
y
a
p
p
lies
a
u
n
if
o
r
m
p
r
o
b
ab
ilit
y
f
ac
t
o
r
of
1
/
in
all
t
h
e
elem
en
ts
of
th
e
c
o
lu
m
n
v
ec
to
r
.
It
can
be
s
ee
n
th
at
m
o
s
t
of
th
e
elem
en
ts
in
th
e
m
atr
ix
b
elo
n
g
s
to
th
at
in
d
ic
ates
th
e
p
o
s
s
ib
ilit
y
of
ju
m
p
in
g
f
r
o
m
o
n
e
p
ag
e
to
an
o
th
er
with
o
u
t
f
o
llo
win
g
a
lin
k
.
Her
e
th
e
tr
an
s
itio
n
p
r
o
b
ab
ilit
y
m
atr
ix
is
co
m
p
u
ted
co
n
s
id
er
in
g
th
e
th
eo
r
y
of
Ma
r
k
o
v
ch
ain
.
T
h
e
c
h
ar
ac
ter
is
tics
of
th
is
is
th
at
th
eir
elem
en
t
lies
b
etwe
en
{
0
,
1
}
an
d
its
co
lu
m
n
s
s
u
m
is
co
m
p
u
ted
as
1
.
T
h
e
s
tu
d
y
f
u
r
th
er
also
ap
p
lies
an
o
th
er
ex
p
licit
f
u
n
ctio
n
al
m
o
d
elin
g
ƒ
2
(
)
to
co
m
p
u
te
.
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
f
u
r
t
h
er
a
ls
o
em
p
lo
y
s
a
m
eth
o
d
o
lo
g
y
of
Per
r
o
n
–
Fro
b
e
n
iu
s
th
eo
r
em
[
4
0
]
to
th
e
m
atr
ix
wh
ich
is
r
etain
ed
.
T
h
e
s
tu
d
y
h
er
e
ap
p
lies
an
o
th
er
f
u
n
ctio
n
al
m
o
d
u
le
f
3
(
x
)
to
co
m
p
u
te
t
h
e
non
-
ze
r
o
s
o
lu
tio
n
.
T
h
e
s
tu
d
y
also
ex
p
lo
r
es
th
e
b
e
s
t
way
to
o
p
tim
ize
th
e
co
m
p
u
t
atio
n
of
p
a
g
e
r
an
k
s
tr
ateg
y
by
ex
p
lo
r
in
g
ad
v
a
n
tag
e
of
p
ar
ticu
lar
s
tr
u
ct
u
r
e
of
Ma
r
k
o
v
m
atr
ix
.
He
r
e
th
e
ap
p
r
o
ac
h
attem
p
ts
to
p
r
eser
v
e
th
e
s
p
ar
s
i
ty
f
ac
to
r
ass
o
ciate
d
with
.
T
h
e
f
o
r
m
u
latio
n
of
t
h
e
co
m
p
u
tatio
n
of
tr
a
n
s
itio
n
m
atr
ix
ca
n
b
e
f
o
r
m
e
d
as
(
3
)
:
T
=
∑
(
P
∗
V
∗
Dia
g
,
ε
)
(
3
)
Her
e
r
ep
r
esen
ts
a
d
ia
g
o
n
al
m
atr
ix
(
)
co
n
s
id
er
i
n
g
t
h
e
r
ec
ip
r
o
c
als
of
th
e
out
-
d
eg
r
ee
s
.
Als
o
im
p
lies
a
r
an
k
o
n
e
m
atr
ix
wh
i
ch
is
ac
co
u
n
ted
f
o
r
th
e
r
an
d
o
m
ch
o
ices
of
web
p
ag
es
th
at
do
not
f
o
llo
w
th
e
lin
k
s
.
Her
e
th
e
p
ag
e
r
an
k
s
tr
ateg
y
ca
n
be
o
p
tim
ized
with
th
e
(
4
)
.
(
I
−
P
∗
V
∗
Dia
g
)
x
=
κ
(
4
)
Her
e
is
th
e
v
ec
to
r
of
all
o
n
es
an
d
co
r
r
esp
o
n
d
to
.
T
h
e
p
r
o
g
r
ess
iv
e
co
m
p
u
tatio
n
of
p
a
g
e
r
a
n
k
in
g
can
be
f
u
r
th
er
u
p
d
ate
d
with
r
e
s
p
ec
t
to
th
e
f
o
llo
win
g
n
o
r
m
alize
d
ex
p
r
ess
io
n
:
r
=
∑
(
1
−
P
r
)
n
,
P
r
×
(
M
′
×
(
r
.
d
)
+
s
n
)
(
5
)
Her
e
d
en
o
tes
a
v
ec
to
r
c
o
n
s
is
tin
g
of
p
ag
e
r
a
n
k
s
co
r
es
wh
er
ea
s
im
p
lies
a
s
ca
lar
d
u
m
p
in
g
f
ac
to
r
an
d
its
v
alu
e
is
co
n
s
id
er
ed
to
be
0
.
8
5
.
T
h
is
p
r
o
b
a
b
ilit
y
f
ac
to
r
in
d
i
ca
tes
th
e
p
o
s
s
ib
ilit
y
of
a
u
s
er
to
click
on
a
lin
k
on
a
cu
r
r
e
n
t
p
ag
e
r
ath
er
co
n
tin
u
i
n
g
to
an
o
th
er
r
an
d
o
m
p
a
g
e.
H
er
e
′
r
ep
r
esen
ts
an
a
d
jace
n
cy
m
atr
ix
of
th
e
web
g
r
ap
h
s
tr
u
ctu
r
e
.
Als
o
,
th
e
ve
cto
r
d
in
d
icate
s
th
e
out
-
d
eg
r
ee
m
e
asu
r
e
of
a
node
in
th
e
g
r
a
p
h
s
tr
u
ctu
r
e.
T
h
e
v
alu
e
of
d
is
co
n
s
id
er
ed
to
be
1,
if
th
e
r
e
ex
is
t
n
o
d
es
with
no
o
u
t
g
o
in
g
lin
k
s
.
r
ep
r
esen
ts
th
e
s
ca
lar
n
u
m
b
er
of
n
o
d
es
in
th
e
g
r
a
p
h
.
Her
e
in
ex
p
r
ess
io
n
(
5)
,
r
ep
r
esen
ts
s
u
m
of
th
e
p
a
g
e
r
an
k
s
co
r
es
f
o
r
t
h
e
p
ag
es
h
av
in
g
no
lin
k
s
.
2
.
3
.
Alg
o
rit
hm
des
ig
n
f
o
r
pa
g
e
r
a
nk
ing
in
s
ea
rc
h e
ng
ine o
ptim
iza
t
io
n
T
h
e
f
o
llo
win
g
an
al
y
tical
alg
o
r
ith
m
ex
h
i
b
its
th
e
wo
r
k
f
lo
w
m
o
d
el
of
th
e
p
r
o
p
o
s
ed
alg
o
r
it
h
m
d
esig
n
s
tr
ateg
y
of
p
ag
e
r
an
k
in
g
f
o
r
SEO.
Her
e
s
tu
d
y
f
o
r
m
u
lates
s
im
p
lifie
d
wo
r
k
f
l
o
w
m
o
d
elin
g
of
th
e
d
esig
n
of
p
ag
e
r
an
k
in
g
s
tr
ateg
y
f
o
r
ef
f
ec
tiv
e
SEO.
T
h
e
s
tu
d
y
also
in
co
r
p
o
r
ates
a
s
et
of
ex
p
licit
f
u
n
ctio
n
alies
to
m
o
d
el
th
e
d
esig
n
s
tr
ateg
y
of
SEO
wh
er
e
a
s
et
of
b
aselin
e
s
tr
ateg
ies
ar
e
also
r
ef
f
er
ed
f
o
r
o
p
tim
ized
e
x
ec
u
tio
n
.
T
h
e
s
tep
s
ass
o
ciate
d
with
th
e
p
r
o
p
o
s
ed
r
an
k
in
g
s
tr
ateg
y
ar
e
illu
s
tr
ated
in
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
1:
Fo
r
p
ag
e
r
an
k
i
n
g
f
o
r
ef
f
ec
tiv
e
SEO
I
n
p
u
t:
s
o
u
r
ce
(
s
)
,
ta
r
g
et
(
t)
Ou
tp
u
t:
r
an
k
r
Star
t
1.
I
n
it
s
,
t
2.
{
W
i
}
i
=
1
n
f
o
r
s
,
t
3.
Fo
r
i=1
:s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
5
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3
8
I
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14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
73
-
82
78
4.
Fo
r
j
=
1
:t
5.
V
n
×
n
,
I
l
i
n
k
,
O
l
i
n
k
,
d
ig
r
ap
h
6.
E
x
ec
u
te
ƒ
1
(
)
7.
r
ow
i
,
c
ol
j
P
r
,
1
−
P
r
,
δ
8.
ƒ
2
(
)
to
co
m
p
u
te
T
ij
(
Ma
r
ko
v
C
h
a
in
)
9.
P
err
o
n
–
F
r
o
b
en
iu
s
th
eo
r
em
[
4
0
]
10.
Op
tim
ize
tr
an
s
itio
n
m
atr
ix
T
11.
(
I
−
P
∗
V
∗
Dia
g
)
x
=
κ
(
5
)
12.
C
o
m
p
u
te
Pag
e
R
an
k
r
f
o
r
SEO
u
s
in
g
(
5
)
13.
No
r
m
aliza
tio
n
of
each
r
14.
R
etain
Sig
n
if
ican
t
Pag
e
R
an
k
Sco
r
e
15.
E
n
d
E
n
d
T
h
e
ab
o
v
e
an
aly
tical
o
p
e
r
atio
n
s
in
v
o
lv
ed
in
th
e
p
r
o
p
o
s
ed
p
a
g
e
r
an
k
in
g
s
tr
ateg
y
ar
e
ap
p
lied
o
v
er
SEO
f
o
r
ef
f
ec
tiv
e
SE
r
esu
lts
f
o
r
tar
g
et
an
d
r
elev
an
t
to
p
p
a
g
e
r
etr
iev
al.
T
h
e
s
tu
d
y
also
ap
p
lies
f
o
r
m
u
latio
n
of
d
ir
ec
ted
g
r
ap
h
s
tr
u
ctu
r
e
to
v
is
u
alize
th
e
web
s
tr
u
ctu
r
e
m
o
d
el
to
illu
s
tr
ate
how
ea
c
h
node
r
ep
r
esen
tin
g
web
p
ag
e
co
n
f
er
s
its
s
p
ec
if
ic
r
an
k
s
co
r
e
to
o
th
er
n
o
d
es
or
we
b
p
ag
es.
U
n
lik
e
ex
is
tin
g
p
ag
e
r
an
k
s
tr
ateg
ies
(
HI
T
S
an
d
Pag
eRan
k
),
th
e
p
r
o
p
o
s
ed
id
ea
of
th
e
s
im
p
lifie
d
an
d
lig
h
t
-
weig
h
t
an
aly
ti
ca
l
f
r
am
ewo
r
k
of
SEO
co
n
s
id
er
in
g
th
e
o
p
tim
ized
p
ag
e
r
a
n
k
s
tr
ateg
y
n
o
t
o
n
ly
en
h
an
ce
th
e
r
etr
iev
al
p
er
f
o
r
m
an
ce
,
also
th
e
r
ed
u
ce
d
iter
atio
n
s
f
o
r
o
p
tim
al
p
r
o
ce
s
s
in
g
of
t
h
e
wo
r
k
f
lo
w
m
o
d
el
en
s
u
r
es
ef
f
ec
tiv
e
r
etr
iev
a
l
tim
e.
T
h
e
n
o
v
elty
of
th
e
p
r
o
p
o
s
ed
p
ag
e
r
an
k
alg
o
r
ith
m
of
SEO
is
as
f
o
llo
ws:
‒
Un
lik
e
ex
is
tin
g
p
ag
e
r
an
k
s
tr
ateg
ies,
th
e
p
r
o
p
o
s
ed
p
a
g
e
r
an
k
alg
o
r
ith
m
c
o
n
tr
ib
u
tes
to
war
d
s
en
h
an
cin
g
t
h
e
r
etr
iev
al
ef
f
icien
cy
of
SE
f
o
r
t
ar
g
et
to
p
-
k
p
a
g
es.
‒
T
h
e
d
esig
n
id
ea
is
s
im
p
lifie
d
f
o
r
lig
h
t
-
weig
h
t
a
n
aly
tical
o
p
er
atio
n
s
wh
ic
h
also
e
n
s
u
r
e
s
co
s
t
ef
f
ec
tiv
e
co
m
p
u
tatio
n
an
d
s
h
o
r
ter
r
etr
ie
v
al
tim
e
f
o
r
SEO
‒
Un
lik
e
ex
is
tin
g
s
y
s
tem
,
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
h
an
d
les
th
e
p
r
o
b
lem
of
to
p
ic
d
r
if
t
a
n
d
s
et
h
ig
h
r
a
n
k
v
al
u
es
to
more
p
o
p
u
lar
p
a
g
es
f
o
r
e
f
f
e
ctiv
e
s
ea
r
ch
en
g
in
e
r
esu
lts
on
th
e
to
p
of
th
e
f
ir
s
t
p
a
g
e.
A
clo
s
er
lo
o
k
in
to
th
e
en
tire
alg
o
r
ith
m
im
p
lem
en
tatio
n
s
h
o
ws
th
at
th
e
p
r
o
p
o
s
ed
s
ch
em
e
o
f
f
er
s
a
n
o
v
el
a
n
d
s
o
p
h
is
ticated
SEO
o
p
er
atio
n
s
with
b
alan
ce
d
p
er
f
o
r
m
an
ce
b
e
twee
n
r
etr
iev
al
ef
f
icien
cy
a
n
d
r
etr
iev
al
tim
e.
T
h
e
n
ex
t
s
ec
tio
n
f
u
r
th
er
illu
s
tr
ates
th
e
ex
p
er
im
en
tal
o
u
tco
m
e
o
b
tain
ed
f
r
o
m
a
s
tr
ateg
ic
im
p
lem
en
tatio
n
of
th
e
f
o
r
m
u
lated
o
p
tim
ized
p
ag
e
r
a
n
k
alg
o
r
ith
m
of
SEO.
3.
RE
SU
L
T
ANAL
YSI
S
T
h
is
s
ec
tio
n
illu
s
tr
ates
th
e
n
u
m
er
ical
o
u
tco
m
e
o
b
tain
e
d
af
te
r
s
im
u
latin
g
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
o
v
e
r
s
ix
d
if
f
er
en
t
web
s
ites
.
T
h
e
p
r
i
m
e
r
ea
s
o
n
b
eh
in
d
ad
o
p
tin
g
an
aly
tical
s
tr
ateg
y
f
o
r
n
u
m
er
ical
s
im
u
latio
n
is
–
it
p
r
o
v
id
es
b
etter
r
ep
r
esen
tatio
n
of
th
e
o
u
tco
m
e
co
n
s
id
er
i
n
g
d
if
f
er
en
t
m
etr
ics
th
r
o
u
g
h
wh
ich
t
h
e
ef
f
ec
ti
v
en
ess
of
th
e
p
r
o
p
o
s
ed
s
tr
ateg
y
co
u
l
d
be
v
alid
ated
to
a
g
r
ea
ter
ex
ten
t.
T
h
e
r
esu
lt
a
n
aly
s
is
also
co
v
er
s
th
e
s
im
u
latio
n
ass
es
s
m
en
t
s
tr
ate
gy
alo
n
g
wit
h
ex
p
e
r
im
en
tal
o
u
tco
m
e
an
d
a
n
aly
s
is
to
co
n
clu
d
e
th
e
ef
f
ec
ti
v
en
ess
of
t
h
e
s
tu
d
y
m
o
d
el.
3
.
1
.
Sim
ula
t
io
n
a
s
s
esm
ent
s
t
ra
t
eg
y
T
h
e
s
tu
d
y
co
n
s
id
er
s
MA
T
L
AB
to
co
n
s
tr
u
ct
th
e
f
r
am
ewo
r
k
m
o
d
elin
g
f
o
r
p
r
o
p
o
s
ed
o
p
ti
m
ized
p
ag
e
r
an
k
in
g
of
SEO.
It
co
n
s
id
er
s
a
r
eg
u
lar
64
-
b
it
W
in
d
o
ws
m
ac
h
in
e
with
i5
p
r
o
ce
s
s
in
g
ca
p
ab
i
lity
.
T
h
e
alg
o
r
ith
m
is
s
tr
ateg
ically
m
o
d
eled
an
d
s
cr
ip
ted
co
n
s
id
er
in
g
an
aly
tical
s
ch
em
a
to
r
ea
lis
e
th
e
o
b
jecti
v
e
of
th
e
p
r
o
p
o
s
ed
r
esear
ch
s
tu
d
y
.
T
h
e
n
u
m
er
ical
an
aly
s
is
is
co
n
s
id
er
ed
to
co
m
p
u
te
th
e
v
alu
es
an
d
to
v
is
u
a
lize
th
e
o
u
tco
m
e
as
o
b
tain
ed
f
r
o
m
th
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
.
Fo
r
th
e
p
u
r
p
o
s
e
of
p
er
f
o
r
m
a
n
ce
ass
ess
m
en
t
of
th
e
p
r
o
p
o
s
ed
s
tr
ateg
ic
s
ch
em
a
of
p
a
g
e
r
an
k
in
g
t
h
e
s
t
u
d
y
not
o
n
l
y
r
elies
on
ev
alu
at
in
g
th
e
p
a
g
e
r
an
k
s
co
r
e
f
o
r
w
eb
p
a
g
es
b
u
t
it
also
co
n
s
id
er
s
co
m
p
u
tatio
n
al
tim
e
as
a
p
ar
am
eter
of
co
m
p
lex
ity
in
th
e
f
o
r
m
of
n
u
m
b
er
of
iter
atio
n
s
of
th
e
alg
o
r
ith
m
to
ju
d
g
e
th
e
how
it
co
n
v
er
g
es
to
war
d
s
th
e
tar
g
eted
to
p
-
r
etr
ie
v
al
of
p
ag
es
in
SE
r
esu
lts
in
co
n
s
id
er
ab
le
am
o
u
n
t
of
r
etr
ie
v
al
tim
e.
T
h
e
ex
p
er
i
m
en
tal
o
u
tco
m
e
is
f
u
r
th
er
ass
ess
ed
f
o
r
a
co
m
p
ar
ativ
e
s
tu
d
y
u
n
d
er
d
if
f
e
r
en
t
co
n
d
itio
n
s
.
3
.
2
.
E
x
perim
ent
a
l
a
s
s
esm
ent
a
nd
a
na
ly
s
is
T
h
e
s
tr
ateg
y
f
o
r
r
esu
lt
an
aly
s
i
s
co
n
s
id
er
s
im
p
lem
en
tin
g
th
is
p
r
o
p
o
s
ed
p
ag
e
r
an
k
id
ea
to
e
n
h
an
ce
t
h
e
s
ea
r
ch
en
g
in
e
p
er
f
o
r
m
an
ce
f
o
r
b
o
th
r
etr
iev
al
ef
f
icien
cy
with
r
esp
ec
t
to
r
an
k
in
g
an
d
r
e
d
u
cin
g
th
e
co
m
p
u
tatio
n
al
tim
e
to
m
in
im
ize
th
e
tim
e
of
r
etr
iev
al.
T
h
e
Fig
u
r
e
1
s
h
o
ws
th
e
p
ag
e
r
an
k
m
ea
s
u
r
e
o
b
tain
ed
th
r
o
u
g
h
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
a
g
e
r
a
n
k
-
b
a
s
ed
a
n
a
lytica
l
d
esig
n
o
f
effec
tive
s
ea
r
ch
en
g
i
n
e
o
p
timiz
a
tio
n
…
(
V
in
u
th
a
M
yso
r
e
S
r
in
iva
s
)
79
p
r
o
p
o
s
ed
p
ag
e
r
a
n
k
alg
o
r
it
h
m
of
SEO
to
war
d
s
r
etr
iev
in
g
th
e
to
p
-
8
p
ag
es
f
r
o
m
th
e
SE.
T
h
e
d
eg
r
ee
of
in
f
o
r
m
atio
n
of
n
o
d
e
co
m
p
u
tes
th
e
av
er
ag
e
I
n
Deg
r
ee
an
d
Ou
t
Deg
r
ee
m
ea
s
u
r
e
ar
e
20
an
d
14
r
esp
ec
tiv
ely
f
o
r
th
e
r
etr
iev
ed
p
ag
es.
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
also
f
u
r
th
er
n
o
r
m
aliz
es
th
e
p
ag
e
r
an
k
s
co
r
e
of
th
e
in
d
iv
id
u
al
p
ag
es
to
en
h
an
ce
th
e
SE
r
esu
lts
co
n
s
id
er
in
g
th
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r
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es
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d
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h
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ates
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o
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m
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m
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lex
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ef
l
ec
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th
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alg
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ith
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’
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in
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lu
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ce
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n
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izin
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e
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e
Fig
u
r
e
2
s
h
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th
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m
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aly
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is
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ith
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ith
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r
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s
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ec
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T
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e
Fig
u
r
e
2
s
h
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ws
th
at
th
e
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p
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s
ed
o
p
ti
m
ized
Pag
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k
s
tr
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co
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g
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d
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m
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m
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s
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ed
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im
p
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a
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ce
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ed
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s
h
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y
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o
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r
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at
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p
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h
ig
h
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r
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ta
r
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n
SE.
Evaluation Warning : The document was created with Spire.PDF for Python.
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g
as c
o
m
p
ar
is
o
n
to
th
e
e
x
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tin
g
Pag
eRan
k
s
tr
ateg
y
.
Fig
u
r
e
2.
C
o
m
p
a
r
ativ
e
s
tu
d
y
of
co
m
p
u
tin
g
tim
e
m
ea
s
u
r
e
f
o
r
iter
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n
s
T
ab
le
1
.
C
o
m
p
a
r
is
o
n
with
s
tate
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of
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ar
t
M
e
t
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d
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o
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p
u
t
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t
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Hi
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4.
CO
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SI
O
N
T
h
e
s
tu
d
y
in
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is
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k
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tr
o
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ce
s
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o
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ize
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o
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ith
m
f
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ef
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d
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ased
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alize
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er
f
o
r
m
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ce
of
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r
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n
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h
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et
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s
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r
k
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v
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s
m
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g
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n
d
th
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Per
r
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n
–
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b
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s
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em
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er
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etter
s
ea
r
ch
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er
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lik
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th
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e
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y
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s
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ce
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d
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In
th
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n
d
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d
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m
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r
es
th
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er
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o
r
m
an
ce
of
t
h
e
p
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p
o
s
ed
Pag
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k
s
tr
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y
with
t
he
s
tate
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of
-
th
e
-
ar
t
p
ag
e
r
an
k
d
esig
n
s
.
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ju
s
tifie
s
th
e
p
r
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p
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s
ed
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s
tr
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'
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ef
f
ec
tiv
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ess
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en
h
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g
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er
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o
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m
an
ce
.
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tu
r
e
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