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m
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[
1
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[
2
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[
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9
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,
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I
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t
r
elate
d
w
o
r
k
s
.
Se
ctio
n
3
f
o
cu
s
es
o
n
t
h
e
g
r
ap
h
c
lu
s
ter
i
n
g
tech
n
iq
u
e.
Sectio
n
4
d
escr
ib
es
P
o
liti
ca
l
B
lo
g
o
s
p
h
er
e
Net
w
o
r
k
.
Sectio
n
5
in
tr
o
d
u
ce
s
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
.
Sectio
n
6
r
ev
ie
w
s
th
e
e
x
p
er
i
m
e
n
tal
r
es
u
lt
s
.
Fin
all
y
,
s
ec
t
io
n
7
co
n
clu
d
es t
h
is
p
ap
er
.
2.
RE
L
AT
E
D
WO
RK
S
T
h
is
s
ec
tio
n
r
ev
ie
w
s
a
s
u
m
m
ar
y
o
f
m
o
s
t
r
ec
e
n
t
r
elate
d
w
o
r
k
s
co
n
ce
r
n
i
n
g
th
e
g
r
ap
h
clu
s
ter
in
g
m
et
h
o
d
s
o
f
s
o
cial
n
e
t
w
o
r
k
s
.
T
h
e
g
o
al
o
f
g
r
ap
h
clu
s
ter
in
g
is
to
g
r
o
u
p
th
e
n
o
d
es
o
f
t
h
e
n
et
w
o
r
k
t
h
at
h
a
v
e
d
en
s
er
co
n
n
ec
tio
n
s
a
m
o
n
g
th
e
m
.
So
m
e
m
et
h
o
d
s
s
u
ch
a
s
C
liq
u
e
P
ec
u
latio
n
Me
th
o
d
s
C
P
M
f
o
cu
s
o
n
in
ter
n
a
l/ex
ter
n
al
ed
g
e
co
u
n
t
i
n
g
[
3
]
w
h
i
le
ig
n
o
r
in
g
t
h
e
in
t
er
ac
tio
n
s
an
d
v
er
tex
c
h
ar
ac
te
r
is
tics
,
i
n
[
1
4
]
th
e
au
th
o
r
s
p
r
o
p
o
s
e
a
cliq
u
e
m
eth
o
d
o
n
co
-
p
u
r
ch
ased
n
et
wo
r
k
w
ei
g
h
ted
g
r
ap
h
,
to
f
i
n
d
m
icr
o
-
cl
u
s
ter
,
th
e
alg
o
r
ith
m
w
o
r
k
s
in
t
w
o
p
h
ases
g
r
ap
h
p
o
lis
h
i
n
g
to
e
n
u
m
er
ate
i
n
ter
s
ec
tio
n
s
o
f
n
ei
g
h
b
o
r
s
a
n
d
cliq
u
e
en
u
m
er
atio
n
to
co
u
n
t
m
a
x
i
m
u
m
cliq
u
e
s
.
Ne
w
m
a
n
-
Gir
v
a
n
i
s
co
n
s
id
er
ed
as
w
ell
-
k
n
o
w
n
d
iv
i
s
iv
e
al
g
o
r
ith
m
[
1
5
]
f
o
r
co
m
m
u
n
it
y
d
etec
tio
n
w
h
ic
h
b
ased
o
n
t
w
o
m
a
in
s
t
ep
s
;
f
ir
s
t
d
etec
ts
s
o
m
e
ed
g
es
b
ased
o
n
b
et
w
ee
n
n
es
s
m
ea
s
u
r
e
th
e
n
s
p
lits
th
e
n
et
w
o
r
k
i
n
to
co
m
m
u
n
itie
s
b
as
ed
o
n
th
e
d
etec
ted
ed
g
es
f
i
n
all
y
it r
eq
u
ir
es b
et
w
ee
n
n
e
s
s
r
ec
alcu
latio
n
a
f
ter
ea
c
h
s
p
litt
i
n
g
,
t
h
e
q
u
alit
y
o
f
t
h
e
co
m
m
u
n
itie
s
i
s
m
ea
s
u
r
ed
u
s
in
g
th
e
m
a
x
i
m
al
m
o
d
u
lar
it
y
.
Ho
wev
er
,
th
e
m
e
th
o
d
i
s
n
o
t su
itab
le
f
o
r
lar
g
e
n
e
t
w
o
r
k
s
an
d
it su
f
f
er
s
f
r
o
m
th
e
r
eso
l
u
t
io
n
li
m
it.
I
n
A
B
C
D
[
6
]
,
th
e
au
th
o
r
s
in
tr
o
d
u
ce
d
n
e
w
a
lg
o
r
it
h
m
b
ased
o
n
b
i
-
d
ir
ec
tio
n
al
co
n
n
ec
t
io
n
s
an
d
n
o
d
e
s
f
ea
t
u
r
es
to
d
etec
t
co
m
m
u
n
it
y
attr
ac
tiv
en
e
s
s
o
f
OSN,
t
h
e
alg
o
r
ith
m
w
as
v
a
lid
ated
in
S
NA
P
p
latf
o
r
m
a
n
d
co
m
p
ar
ed
w
it
h
C
NM
[
5
]
,
ac
co
r
d
in
g
to
t
h
e
r
esear
c
h
er
AB
C
D
is
o
u
tp
er
f
o
r
m
ed
C
NM
an
d
it
ca
n
d
is
co
v
er
s
m
al
ler
co
m
m
u
n
ities
i
n
co
n
tr
ast
w
ith
C
NM
,
h
o
w
e
v
er
,
th
e
m
et
h
o
d
w
as
n
o
t
s
h
o
w
n
t
h
e
co
m
p
ar
at
iv
e
r
es
u
lts
o
f
th
e
m
o
d
u
lar
it
y
v
al
u
es to
p
r
o
v
e
its
ef
f
ec
ti
v
e
n
ess
.
T
h
e
k
-
p
r
o
to
t
y
p
e
alg
o
r
it
h
m
I
SC
D+
[
1
6
]
an
iter
ati
v
e
m
o
d
el
f
o
r
f
a
s
t
g
r
ap
h
cl
u
s
ter
i
n
g
,
th
e
a
u
th
o
r
s
in
tr
o
d
u
ce
a
n
e
w
id
ea
f
o
r
d
etec
tin
g
co
m
m
u
n
itie
s
,
th
e
al
g
o
r
ith
m
i
m
p
o
s
es
t
w
o
f
ac
to
r
s
n
a
m
e
l
y
lo
ca
l
i
m
p
o
r
tan
ce
an
d
i
m
p
o
r
tan
ce
co
n
ce
n
tr
atio
n
to
s
elec
t n
o
d
es
w
it
h
d
if
f
er
en
t
w
ei
g
h
ts
to
r
ep
r
esen
t c
o
m
m
u
n
i
ties
.
T
h
e
KNN
-
b
ased
alg
o
r
ith
m
s
in
[
1
2
]
th
e
au
th
o
r
s
p
r
o
p
o
s
e
a
d
ir
ec
ted
w
eig
h
ted
g
r
ap
h
clu
s
ter
i
n
g
alg
o
r
ith
m
f
o
r
co
m
m
u
n
i
t
y
d
et
ec
tio
n
,
th
e
alg
o
r
it
h
m
co
n
s
id
e
r
s
n
et
w
o
r
k
to
p
o
lo
g
y
o
n
l
y
a
n
d
it
is
s
ig
n
if
ican
t
l
y
f
o
cu
s
ed
o
n
th
e
p
ath
tr
av
er
s
e
d
f
r
eq
u
en
c
y
a
n
d
n
ei
g
h
b
o
r
h
o
o
d
n
o
d
es,
n
ev
er
th
ele
s
s
,
t
h
e
m
eth
o
d
s
u
f
f
er
s
f
r
o
m
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
s
i
n
ce
it is
b
ased
o
n
k
-
n
ea
r
es
t n
ei
g
h
b
o
r
s
’
co
m
p
u
tatio
n
s
.
I
n
[
1
0
]
th
e
au
th
o
r
s
i
n
tr
o
d
u
ce
d
a
n
e
w
ap
p
r
o
ac
h
f
o
r
co
m
m
u
n
i
t
y
d
etec
tio
n
i
n
s
o
cial
n
et
w
o
r
k
w
eb
s
i
tes.
be
s
id
es
s
tr
u
ct
u
r
e
s
i
m
ilar
it
y
a
f
r
eq
u
en
t
p
atter
n
m
in
in
g
o
f
n
o
d
es
co
n
ten
t
s
w
as
co
n
tr
ib
u
t
ed
,
th
e
alg
o
r
ith
m
i
s
i
m
p
le
m
en
ted
i
n
f
o
u
r
s
tep
s
,
p
r
ep
r
o
ce
s
s
in
g
,
f
r
eq
u
en
t
p
at
ter
n
co
m
p
u
tin
g
to
o
b
tain
h
ar
m
o
n
io
u
s
g
r
o
u
p
s
,
ex
ten
d
i
n
g
h
ar
m
o
n
io
u
s
g
r
o
u
p
s
in
to
s
m
al
l
co
m
m
u
n
ities
,
f
i
n
a
ll
y
s
m
a
ll
co
m
m
u
n
itie
s
ex
p
a
n
s
io
n
,
h
o
w
e
v
er
,
t
h
e
m
et
h
o
d
s
u
f
f
er
s
f
r
o
m
s
o
m
e
d
is
ad
v
an
ta
g
es
s
u
c
h
as,
ti
m
e
co
m
p
lex
it
y
w
h
ic
h
is
ca
u
s
ed
b
y
th
e
in
p
u
t
p
ar
a
m
eter
s
,
a
tr
ial,
an
d
er
r
o
r
c
o
n
ce
p
t
w
as
u
s
ed
to
d
eter
m
in
e
t
h
e
ap
p
r
o
p
r
iat
e
p
ar
am
eter
s
.
R
o
y
et
a
l
.
in
[
1
7
]
p
r
o
p
o
s
ed
a
g
r
ap
h
-
b
ased
s
p
ec
tr
al
clu
s
ter
i
n
g
m
o
d
el,
th
e
m
et
h
o
d
u
s
es
n
o
v
el
af
f
i
n
it
y
m
atr
i
x
f
o
r
s
p
atial
clu
s
ter
in
g
w
it
h
Ma
h
alan
o
b
is
d
is
ta
n
ce
,
h
o
w
ev
er
,
th
e
m
et
h
o
d
h
as
s
o
m
e
li
m
itatio
n
s
,
th
e
d
is
tan
ce
m
etr
ic
ca
n
o
n
l
y
m
ea
s
u
r
e
f
r
o
m
a
s
i
n
g
le
p
o
in
t,
t
h
is
r
e
d
u
ce
s
r
esu
lts
q
u
al
it
y
.
J
in
ar
at
et
a
l
.
in
[
1
8
]
h
av
e
i
n
tr
o
d
u
ce
d
a
g
r
ap
h
cl
u
s
ter
i
n
g
al
g
o
r
ith
m
f
o
r
w
eb
s
ea
r
c
h
r
es
u
lts
,
th
e
co
r
e
id
ea
o
f
th
e
m
e
th
o
d
is
to
co
m
b
in
e
w
eb
s
ea
r
c
h
r
es
u
lt
s
w
it
h
e
x
ter
n
al
k
n
o
w
led
g
e
d
ata
f
r
o
m
W
ik
ip
ed
ia
to
attain
b
etter
clu
s
ter
in
g
q
u
alit
y
.
t
h
e
m
et
h
o
d
u
s
es
g
r
ap
h
-
b
ased
co
n
s
tr
u
ctio
n
f
o
r
tex
t
clu
s
ter
in
g
to
co
n
n
ec
t
r
elate
d
d
o
cu
m
en
ts
,
n
e
v
er
th
ele
s
s
,
th
e
s
i
m
ilar
it
y
th
r
es
h
o
ld
p
ar
a
m
eter
f
o
r
s
u
b
g
r
ap
h
d
etec
tio
n
m
u
s
t b
e
in
a
ce
r
tai
n
r
an
g
e,
w
h
e
n
th
e
t
h
r
es
h
o
ld
p
ar
am
eter
in
cr
ea
s
es,
t
h
e
cl
u
s
ter
i
n
g
q
u
alit
y
d
ec
r
ea
s
es.
3.
G
RAP
H
C
L
US
T
E
R
I
N
G
T
E
CH
NIQU
E
An
i
n
d
ir
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t
w
eig
h
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ap
h
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ib
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at
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o
m
e
to
p
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lo
g
ical
s
tr
u
c
tu
r
es
an
d
attr
ib
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tes
s
i
m
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it
y
m
ea
s
u
r
es,
th
e
co
m
m
u
n
i
ties
s
h
o
u
ld
h
av
e
t
h
e
f
o
llo
w
i
n
g
asp
ec
ts
;
a.
Si
m
i
lar
v
er
tice
s
s
h
o
u
ld
b
e
p
ar
ticip
ated
in
a
s
i
m
ilar
g
r
o
u
p
,
w
h
ile
t
h
e
d
is
s
i
m
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o
n
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s
s
h
o
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ld
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to
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if
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er
en
t
g
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u
p
s
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b.
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h
e
v
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at
b
elo
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g
to
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co
m
m
u
n
it
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s
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ld
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s
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ar
s
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co
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n
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ted
to
th
e
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th
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v
er
tices
w
it
h
i
n
d
if
f
er
en
t c
o
m
m
u
n
itie
s
.
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h
e
g
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o
f
th
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p
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p
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s
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alg
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ith
m
is
to
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tr
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d
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ce
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w
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h
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ea
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u
r
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h
u
s
ca
n
e
f
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ec
tiv
el
y
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lect
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th
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ar
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is
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et
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to
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n
d
v
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ea
t
u
r
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to
s
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t
h
e
n
t
h
e
s
i
m
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it
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co
h
esi
v
e
n
es
s
.
T
h
e
s
tr
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y
o
f
cl
u
s
ter
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n
g
an
d
th
e
s
i
m
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m
ea
s
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e
w
ill b
e
d
is
c
u
s
s
ed
in
t
h
e
n
e
x
t sectio
n
.
3
.
1
.
Co
ntr
a
s
t
co
m
pa
ra
t
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e
m
et
ho
d
W
-
C
l
u
s
ter
[
9
]
is
a
n
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m
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m
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f
S
A
-
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lu
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asp
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y
ap
p
l
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n
g
a
u
n
if
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d
is
tan
ce
m
ea
s
u
r
e
an
d
n
ei
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h
b
o
r
h
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d
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d
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m
w
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k
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s
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f
o
r
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p
u
r
p
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e;
Den
s
it
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[
1
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an
d
E
n
t
r
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p
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[
1
9
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.
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r
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licated
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u
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ted
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[
2
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]
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ef
lect
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all
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vv
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
o
mmu
n
ity
d
etec
tio
n
o
f p
o
liti
ca
l b
lo
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s
n
etw
o
r
k
b
a
s
ed
o
n
s
tr
u
ctu
r
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-
a
ttr
ib
u
te…
(
A
h
med
F
.
A
l
-
Mu
kh
ta
r
)
2125
5.
T
H
E
P
RO
P
O
SE
D
M
E
T
H
O
D
5
.
1
.
B
lo
ck
dia
g
ra
m
I
n
Fig
u
r
e
2
w
e
ca
n
s
ee
t
h
e
S
AS
-
C
l
u
s
ter
b
lo
ck
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iag
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a
m
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Fig
u
r
e
2
.
SAS
-
C
l
u
s
ter
b
lo
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d
iag
r
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m
5
.
2
.
SAS
-
c
lus
t
er
Gr
ap
h
icall
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,
s
o
cial
n
et
w
o
r
k
s
ca
n
b
e
m
o
d
eled
as
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m
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le
x
n
et
w
o
r
k
s
,
w
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b
o
t
h
n
e
t
w
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k
to
p
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y
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d
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te
x
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r
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ties
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n
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e
co
n
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ed
.
T
h
e
r
elatio
n
s
h
i
p
a
m
o
n
g
v
er
tice
s
is
r
ep
r
ese
n
ted
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e
s
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h
e
p
r
o
p
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s
ed
SA
S
-
C
lu
s
ter
alg
o
r
it
h
m
ac
h
ie
v
es t
h
e
f
o
llo
w
i
n
g
p
r
o
p
er
ti
es:
a.
T
h
e
v
er
tices
w
i
th
i
n
th
e
s
a
m
e
clu
s
ter
ar
e
clo
s
e
to
ea
c
h
o
th
er
co
n
ce
r
n
in
g
t
h
e
s
tr
u
ct
u
r
a
l
s
i
m
ilar
it
y
a
n
d
d
is
s
i
m
ilar
to
o
th
er
v
er
tices o
u
t
s
id
e
th
e
cl
u
s
ter
.
b.
v
er
tices
i
n
th
e
s
a
m
e
clu
s
ter
ar
e
clo
s
e
to
ea
ch
o
th
er
in
ter
m
s
o
f
attr
ib
u
te
s
i
m
ilar
it
y
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n
d
f
ar
f
r
o
m
ea
ch
o
th
er
a
m
o
n
g
th
e
d
i
f
f
er
en
t c
l
u
s
ter
s
.
T
h
e
co
r
e
id
ea
is
to
d
ef
i
n
e
th
e
Gr
av
it
y
f
ac
to
r
,
to
id
en
tify
t
h
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p
o
w
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o
f
th
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elatio
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ip
co
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ce
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i
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g
ea
ch
p
air
o
f
d
ir
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tl
y
co
n
n
ec
te
d
v
er
tices in
t
h
e
to
p
o
lo
g
y
o
f
t
h
e
s
tr
u
ct
u
r
e.
Def
ini
t
io
n
1
(
Gra
vity
F
a
cto
r
)
.
I
n
th
e
i
n
d
ir
ec
t
w
ei
g
h
ted
g
r
ap
h
,
th
e
r
elatio
n
b
et
w
ee
n
t
wo
d
ir
ec
tly
co
n
n
ec
ted
v
er
tices e
x
p
o
s
es t
h
e
s
tr
en
g
t
h
o
f
t
h
e
r
elatio
n
s
h
ip
.
L
et
m
v
an
d
n
v
ar
e
t
w
o
d
ir
ec
tl
y
co
n
n
ec
ted
v
er
tices.
()
m
dv
is
th
e
d
eg
r
ee
o
f
t
h
e
v
er
tex
m
v
.
()
i
cv
is
co
n
s
id
er
ed
th
e
clo
s
e
n
es
s
m
ea
s
u
r
e
o
f
i
v
,
w
h
ich
r
e
f
er
s
to
th
e
in
v
er
s
e
s
u
m
o
f
all
s
h
o
r
test
p
ath
s
a
m
o
n
g
i
v
an
d
all
o
th
er
v
er
ti
ce
s
in
t
h
e
g
r
ap
h
.
mn
w
is
th
e
w
ei
g
h
t
ass
o
ciate
d
w
it
h
t
h
e
ed
g
e
(
,
)
mn
e
v
v
.
Th
e
Gr
av
it
y
Fa
cto
r
is
d
ef
in
ed
in
E
q
u
a
tio
n
(
4
)
.
()
1
()
(
,
)
l
n
1
*
,
()
m
m
m
n
m
n
m
n
dv
j
dv
g
v
v
w
v
v
cj
(
4
)
Def
ini
t
io
n
2
(
Mea
n
Gra
vity
)
.
L
et
m
v
an
d
ar
e
t
w
o
d
ir
ec
tl
y
co
n
n
ec
ted
v
er
tices.
Me
a
n
g
r
a
v
it
y
ca
n
b
e
d
ef
in
ed
in
E
q
u
atio
n
(
5
)
.
(
,
)
(
,
)
(
,
)
,
2
m
n
n
m
m
n
m
n
g
v
v
g
v
v
m
v
v
v
v
(
5
)
w
h
er
e
(
)
(
)
,
th
u
s
o
n
e
ca
n
d
eter
m
i
n
e
w
h
ich
v
er
te
x
is
m
o
r
e
i
m
p
o
r
tan
t.
Def
ini
t
io
n
3
(
P
a
th
Deg
r
ee
)
.
Let
m
v
an
d
ar
e
t
w
o
in
d
ir
ec
tl
y
co
n
n
ec
ted
v
er
tices.
Fo
r
a
g
i
v
en
p
at
h
(
,
,
,
,
,
)
12
v
v
v
v
v
m
m
m
m
i
n
,
p
ath
d
eg
r
ee
ca
n
b
e
d
ef
in
ed
in
E
q
u
atio
n
(
6
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
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n
g
,
Vo
l.
9
,
No
.
3
,
J
u
n
e
2
0
1
9
:
2
1
2
1
-
2
1
3
0
2126
(
,
)
(
,
)
,
n
m
v
m
n
m
n
m
n
v
p
v
v
g
v
v
v
v
(
6
)
w
h
er
e
p
ath
is
ta
k
e
n
in
to
ac
co
u
n
t a
s
t
h
e
w
ei
g
h
ted
s
h
o
r
test
p
at
h
b
et
w
ee
n
a
p
air
o
f
in
d
ir
ec
tl
y
co
n
n
ec
ted
v
er
tice
s
.
Stru
ct
u
r
al/
A
ttrib
u
te
Si
m
ilar
i
t
y
(
SAS),
in
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
th
e
J
ac
ca
r
d
s
i
m
ilar
it
y
co
e
f
f
icien
t
is
ad
o
p
ted
t
o
co
m
p
u
te
t
h
e
s
i
m
ila
r
it
y
m
ea
s
u
r
e,
as d
e
f
in
ed
i
n
E
q
u
atio
n
(
7
)
.
||
(
,
)
XY
s
im
X
Y
XY
(
7
)
w
h
er
e
,
XY
ar
e
v
er
tices,
,
|
|
X
Y
V
,
J
ac
ca
r
d
s
i
m
i
lar
it
y
in
th
e
e
q
u
atio
n
(
7
)
is
a
w
e
ll
-
k
n
o
w
n
s
i
m
ilar
i
t
y
m
ea
s
u
r
e,
th
er
e
f
o
r
e
it
h
as
b
ee
n
u
s
ed
to
f
in
d
o
u
t
t
h
e
r
elev
a
n
c
e
a
m
o
n
g
v
er
tices.
T
h
er
e
ar
e
tw
o
m
a
in
s
i
m
ilar
it
y
ca
lcu
latio
n
s
ar
e
tak
e
n
i
n
to
ac
co
u
n
t.
Dir
ec
tl
y
co
n
n
ec
tio
n
E
q
u
atio
n
(
8
)
.
T
o
ca
lcu
late
th
e
s
i
m
ilar
it
y
b
et
w
ee
n
a
p
air
o
f
d
ir
ec
tly
co
n
n
ec
ted
v
er
t
ices.
11
(
,
)
,
mn
mn
m
n
m
n
vv
m
i
n
j
m
n
ij
w
s
im
v
v
v
v
w
w
w
(
8
)
w
h
er
e
,
|
|
.
mn
v
v
V
mi
w
is
th
e
w
ei
g
h
t
o
f
t
h
e
e
d
g
e
b
et
w
ee
n
t
h
e
v
er
te
x
m
v
an
d
all
S
i
v
er
tices
th
at
ar
e
d
ir
ec
tl
y
co
n
n
ec
ted
to
m
v
.
nj
w
is
t
h
e
w
ei
g
h
t
o
f
t
h
e
ed
g
e
b
et
w
ee
n
t
h
e
v
e
r
tex
n
v
an
d
all
S
j
v
er
tice
s
t
h
at
ar
e
d
ir
ec
tl
y
co
n
n
ec
ted
to
n
v
an
d
mn
w
is
t
h
e
as
s
o
ci
ated
w
ei
g
h
t
o
f
th
e
ed
g
e
(
,
)
mn
e
v
v
.
I
n
d
ir
ec
tl
y
co
n
n
ec
tio
n
E
q
u
atio
n
(
9
)
.
T
h
e
s
i
m
ilar
it
y
is
ca
lc
u
lated
b
ased
o
n
th
e
s
h
o
r
test
p
ath
b
et
w
e
en
m
v
an
d
n
v
.
1
(
,
)
(
,
)
,
n
m
v
m
n
l
l
m
n
lv
s
i
m
v
v
s
i
m
v
v
v
v
(
9
)
w
h
er
e
,
|
|
v
v
V
mn
r
ep
r
esen
t th
e
s
i
m
ilar
it
y
b
et
w
ee
n
t
w
o
i
n
d
ir
ec
tl
y
co
n
n
ec
ted
v
er
tices.
Af
ter
o
b
tain
i
n
g
t
h
e
Me
a
n
G
r
av
it
y
E
q
u
a
tio
n
(
5
)
an
d
P
ath
Deg
r
ee
E
q
u
atio
n
(
6
)
,
th
e
s
tr
u
ct
u
r
al
s
i
m
ilar
it
y
i
s
d
ef
i
n
ed
in
E
q
u
at
i
o
n
(
1
0
)
.
(
,
)
(
,
)
,
(
,
)
(
,
)
(
,
)
,
0
,
m
n
m
n
m
n
m
n
S
m
n
m
n
m
n
mn
s
im
v
v
m
v
v
v
v
s
im
v
v
s
im
v
v
p
v
v
v
v
vv
(
10
)
Nex
t,
t
h
e
v
er
tices
at
tr
ib
u
tes
ar
e
co
n
s
id
er
ed
.
E
ac
h
v
er
te
x
is
ch
ar
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ith
m
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s
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ten
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y
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ted
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h
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e
s
tate
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of
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a
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t
m
et
h
o
d
W
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clu
s
ter
[
9
]
th
r
o
u
g
h
w
e
ll
-
k
n
o
w
n
e
v
al
u
ati
n
g
m
ea
s
u
r
e
s
,
Den
s
i
t
y
,
a
n
d
E
n
tr
o
p
y
.
T
h
e
d
en
s
it
y
as
g
iv
e
n
i
n
Eq
u
atio
n
(
1
)
,
r
ef
lect
s
t
h
e
e
x
te
n
t
o
f
h
o
w
ti
g
h
t
s
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u
ct
u
r
e
i
s
co
n
n
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ted
a
m
o
n
g
v
er
tice
s
i
n
ea
c
h
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u
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ter
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th
e
h
i
g
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er
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en
s
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lu
e
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e
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lects
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e
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m
m
u
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it
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ct
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r
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h
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v
e
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s
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h
e
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n
tr
o
p
y
t
h
at
is
d
escr
i
b
ed
in
E
q
u
atio
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(
2
)
an
d
E
q
u
atio
n
(
3
)
,
w
h
ic
h
is
u
s
ed
to
r
ate
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e
attr
ib
u
te
r
elatio
n
s
h
ip
a
m
o
n
g
v
er
tice
s
,
lo
w
e
n
t
r
o
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lect
s
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etter
r
elev
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ce
a
m
o
n
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v
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tice
s
i
n
ea
ch
clu
s
ter
.
Fi
g
u
r
e
3
an
d
F
ig
u
r
e
4
s
h
o
w
th
e
p
er
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o
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m
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ce
o
f
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S
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l
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s
ter
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n
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er
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g
Den
s
it
y
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d
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n
tr
o
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e
t
h
e
n
u
m
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er
o
f
cl
u
s
ter
s
3
,
5
,
7
,
9
k
.
is
s
e
t
i
n
t
h
e
r
an
g
e
[
0
,
1
]
an
d
1
.
T
h
e
alg
o
r
ith
m
i
s
r
u
n
f
o
r
at
least th
r
ee
iter
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n
s
.
Fig
u
r
e
3
,
r
ev
ie
w
s
t
h
e
d
en
s
it
y
v
al
u
es.
W
h
e
n
s
e
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n
g
to
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th
e
d
en
s
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t
y
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al
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e
is
t
h
e
lo
w
e
s
t,
th
i
s
b
ec
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s
e
o
f
th
e
s
i
m
ilar
it
y
o
f
t
h
e
s
tr
u
ct
u
r
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to
p
o
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o
t
tak
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to
ac
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u
n
t.
A
t
3
k
th
e
d
e
n
s
it
y
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6
o
r
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t
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k
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5
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e
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en
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it
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es
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o
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Fig
u
r
e
4
,
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ev
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w
s
th
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e
n
tr
o
p
y
v
alu
e
s
,
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e
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est
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g
iv
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n
v
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w
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α
eq
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to
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in
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a
lg
o
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ith
m
i
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ased
o
n
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attr
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ilar
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n
co
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as
t,
w
h
en
α
eq
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to
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t
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e
g
i
v
en
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v
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es
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w
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r
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t
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s
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ec
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s
e
t
h
e
at
tr
ib
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te
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ilar
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t
ta
k
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u
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t.
A
t
3
,
5
,
7
,
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k
th
e
b
es
t
g
iv
e
n
-
r
es
u
lts
w
h
en
is
s
et
to
0
.
5
o
r
0
.
8
.
W
h
ile
th
e
q
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alit
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o
f
t
h
e
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es
u
lts
te
n
d
s
to
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r
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s
e
w
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en
0
.
8
.
As
ill
u
s
tr
ated
in
Fi
g
u
r
e
3
an
d
Fig
u
r
e
4
,
th
e
b
est p
er
f
o
r
m
an
ce
f
o
r
S
A
S
-
C
l
u
s
ter
w
h
en
is
eith
er
0
.
5
o
r
0
.
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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o
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f p
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l b
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s
n
etw
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r
k
b
a
s
ed
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n
s
tr
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ctu
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ttr
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(
A
h
med
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.
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l
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Mu
kh
ta
r
)
2129
Fig
u
r
e
3
.
I
m
p
ac
t
f
ac
to
r
α
o
n
t
h
e
p
o
liti
ca
l b
lo
g
.
C
lar
if
ie
s
Den
s
it
y
r
es
u
lts
f
o
r
S
AS
-
C
l
u
s
ter
Fig
u
r
e
4
.
I
m
p
ac
t
f
ac
to
r
α
o
n
t
h
e
p
o
liti
ca
l b
lo
g
.
C
lar
if
ies E
n
tr
o
p
y
r
es
u
lt
s
f
o
r
S
AS
-
C
l
u
s
ter
T
o
s
h
o
w
t
h
e
e
f
f
ec
tiv
e
n
es
s
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
SA
S
-
C
lu
s
ter
is
co
m
p
ar
ed
w
it
h
th
e
s
tate
-
of
-
ar
t
m
et
h
o
d
,
W
-
clu
s
ter
.
B
o
th
m
eth
o
d
s
ar
e
test
ed
f
o
r
a
f
ix
ed
n
u
m
b
er
o
f
clu
s
ter
s
3
,
5
,
7
,
9
k
an
d
is
s
et
to
0
.
5
.
Fig
u
r
e
5
an
d
Fi
g
u
r
e
6
ill
u
s
tr
at
e
th
e
co
m
p
ar
is
o
n
r
e
s
u
l
ts
o
f
t
h
e
d
en
s
it
y
an
d
t
h
e
en
tr
o
p
y
r
esp
e
ctiv
el
y
f
o
r
ea
ch
o
f
S
AS
-
C
l
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s
ter
a
n
d
W
-
cl
u
s
ter
.
A
ll
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es
u
lt
s
h
a
v
e
s
h
o
w
n
th
at
S
A
S
-
C
lu
s
ter
o
u
tp
e
r
f
o
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m
ed
W
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C
l
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s
ter
co
n
ce
r
n
i
n
g
t
h
e
d
en
s
it
y
a
n
d
th
e
en
tr
o
p
y
m
ea
s
u
r
es.
Fig
u
r
e
5
.
Den
s
it
y
co
m
p
ar
is
o
n
v
alu
e
s
o
n
p
o
liti
ca
l b
lo
g
s
Fig
u
r
e
6
.
E
n
tr
o
p
y
co
m
p
a
r
i
s
o
n
v
alu
e
s
o
n
p
o
liti
ca
l b
lo
g
s
7.
CO
NCLU
SI
O
N
No
w
ad
a
y
s
,
s
o
cial
n
et
w
o
r
k
s
h
av
e
b
ec
o
m
e
m
o
r
e
i
n
f
l
u
e
n
tial
in
in
d
i
v
id
u
al
’
s
o
p
in
io
n
,
d
ec
is
io
n
s
,
an
d
th
eir
li
f
e
s
t
y
le.
T
h
er
ef
o
r
e,
an
d
w
it
h
th
e
ac
ce
ler
ated
i
n
cr
ea
s
e
i
n
s
o
cial
n
et
w
o
r
k
s
d
ata,
it
is
i
m
p
o
r
ta
n
t
to
ad
o
p
t
a
m
o
r
e
r
eliab
le
g
r
ap
h
clu
s
ter
i
n
g
m
e
th
o
d
s
f
o
r
co
m
m
u
n
it
y
d
etec
tio
n
.
I
n
th
is
p
ap
er
,
a
g
r
ap
h
clu
s
ter
i
n
g
m
et
h
o
d
f
o
r
co
m
m
u
n
it
y
d
etec
tio
n
i
s
p
r
o
p
o
s
ed
.
T
h
e
m
et
h
o
d
in
tr
o
d
u
ce
s
t
w
o
co
n
ce
p
ts
,
Gr
a
v
it
y
d
eg
r
ee
an
d
P
ath
d
eg
r
ee
,
to
in
cr
ea
s
e
t
h
e
s
tr
u
ct
u
r
al
s
i
m
il
ar
ities
w
it
h
i
n
t
h
e
d
etec
ted
co
m
m
u
n
itie
s
.
I
n
ad
d
itio
n
,
t
h
e
ad
o
p
ted
m
et
h
o
d
co
m
b
i
n
es
s
tr
u
ct
u
r
al
s
i
m
ilar
iti
es
w
i
th
t
h
e
m
u
ltip
le
attr
ib
u
te
s
o
f
n
o
d
es
to
attain
m
o
r
e
co
h
esiv
e
n
es
s
s
i
m
ilar
it
y
.
T
h
e
ex
p
er
i
m
e
n
tal
r
e
s
u
l
ts
h
a
v
e
s
h
o
w
n
th
at
S
AS
-
C
l
u
s
ter
is
b
etter
th
a
n
W
-
cl
u
s
ter
ac
co
r
d
in
g
to
De
n
s
it
y
a
n
d
E
n
tr
o
p
y
e
v
al
u
atio
n
m
ea
s
u
r
es.
RE
F
E
R
E
NC
E
S
[1
]
P
.
M
r
u
ty
u
n
jay
a
,
e
t
a
l
.
,
“
S
o
c
ial
Ne
tw
o
rk
in
g
,
”
Bo
o
k
o
f
S
p
rin
g
e
r
In
ter
n
a
ti
o
n
a
l
P
u
b
li
s
h
in
g
,
v
o
l
.
6
5
,
p
p
.
4
5
-
8
3
,
2
0
1
4
.
[2
]
S
.
S
.
El
isa
,
“
G
ra
p
h
Clu
ste
ri
n
g
,
”
Co
mp
u
ter
S
c
ien
c
e
Rev
iew
,
v
o
l.
1
,
p
p
.
2
7
-
64
,
2
0
0
7
.
[3
]
F
.
S
a
n
t
o
a
n
d
H
.
Da
rk
o
,
“
Co
m
m
u
n
it
y
d
e
tec
ti
o
n
in
n
e
tw
o
rk
s:
A
u
se
r
g
u
id
e
,
”
Ph
y
sic
s
Rep
o
rts
,
v
o
l.
6
5
9
,
p
p
.
1
-
4
5
,
2
0
1
6
.
[4
]
G
irv
a
n
M
.
a
n
d
Ne
wm
a
n
M
.
E.
,
“
Co
m
m
u
n
it
y
stru
c
tu
re
in
so
c
ial
a
n
d
b
i
o
lo
g
ica
l
n
e
tw
o
rk
s
,
”
Pro
c
e
e
d
in
g
s
o
f
th
e
n
a
ti
o
n
a
l
a
c
a
d
e
my
o
f
sc
ien
c
e
s
,
v
o
l
.
9
9
,
p
p
.
7
8
2
1
-
7
8
2
6
,
2
0
0
2
.
[5
]
Clau
se
t
,
e
t
a
l
.
,
“
F
i
n
d
i
n
g
c
o
m
m
u
n
it
y
stru
c
tu
re
in
v
e
r
y
larg
e
n
e
t
w
o
rk
s,
”
Ph
y
sic
a
l
re
v
iew E
,
v
o
l.
7
0
,
p
p
.
1
-
6
,
2
0
0
4
.
0
0
.1
0
.2
0
.3
0
.4
0
.5
0
.6
0
.7
0
.8
0
.9
0
0
.
1
0
.
2
0
.
3
0
.
4
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
1
D
E
NS
IT
Y
Α
L
P
H
A
k
=
3
k
=
5
k
=
7
k
=
9
0
0
.1
0
.2
0
.3
0
.4
0
.5
0
.6
0
0
.
1
0
.
2
0
.
3
0
.
4
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
1
E
NT
R
O
P
Y
A
L
P
H
A
k
=
3
k
=
5
k
=
7
k
=
9
0
0
.1
0
.2
0
.3
0
.4
0
.5
0
.6
0
.7
0
.
8
0
.9
3
5
7
9
D
e
n
s
i
t
y
No
.
o
f
c
l
u
s
t
ers
k
S
A
S
-
C
l
u
s
t
e
r
W
-
c
l
u
s
t
e
r
0
0
.1
0
.2
0
.3
0
.4
0
.5
0
.6
0
.7
0
.8
0
.9
3
5
7
9
E
n
t
ro
p
y
N
o
.
o
f
c
l
u
s
t
ers
k
S
A
S
-
C
l
u
s
t
er
W
-
c
l
u
s
t
er
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
3
,
J
u
n
e
2
0
1
9
:
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1
2
1
-
2
1
3
0
2130
[6
]
L
iu
,
e
t
a
l
.
,
“
W
e
ig
h
ted
G
r
a
p
h
Clu
ste
rin
g
f
o
r
Co
m
m
u
n
it
y
De
tec
ti
o
n
o
f
L
a
r
g
e
S
o
c
ial
Ne
t
w
o
rk
s,
”
Pro
c
e
d
ia
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l.
3
1
,
p
p
.
8
5
-
9
4
,
2
0
1
4
.
[7
]
L
a
m
b
io
tt
e
,
e
t
a
l
.
,
“
Ra
n
d
o
m
W
a
lk
s,
M
a
rk
o
v
P
r
o
c
e
ss
e
s
a
n
d
th
e
M
u
lt
isc
a
le
M
o
d
u
lar
Org
a
n
iza
ti
o
n
o
f
Co
m
p
lex
Ne
tw
o
rk
s,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s o
n
Ne
two
rk
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
v
o
l.
1
,
p
p
.
7
6
-
9
0
,
2
0
1
4
.
[8
]
Bo
o
b
a
la
n
,
e
t
a
l
.
,
“
G
ra
p
h
c
lu
st
e
rin
g
u
sin
g
k
-
Ne
ig
h
b
o
u
r
h
o
o
d
A
tt
rib
u
te
S
tru
c
t
u
ra
l
sim
il
a
rit
y
,
”
Ap
p
l
ied
S
o
f
t
Co
mp
u
t
in
g
,
v
o
l
.
4
7
,
p
p
.
2
1
6
-
2
2
3
,
2
0
1
6
.
[9
]
Ch
e
n
g
,
e
t
a
l
.
,
“
Clu
ste
ri
n
g
L
a
rg
e
A
tt
rib
u
ted
G
ra
p
h
s:
A
Ba
lan
c
e
b
e
tw
e
e
n
S
tru
c
t
u
ra
l
a
n
d
A
tt
rib
u
te
S
i
m
il
a
rit
ies
,
”
ACM
T
ra
n
sa
c
ti
o
n
s
o
n
Kn
o
wled
g
e
Disc
o
v
e
ry
fro
m Da
ta
,
v
o
l.
5
,
p
p
.
1
-
3
3
,
2
0
1
1
.
[1
0
]
M
o
o
sa
v
i
,
e
t
a
l
.
,
“
Co
m
m
u
n
it
y
d
e
tec
ti
o
n
i
n
so
c
ial
n
e
tw
o
rk
s
u
sin
g
u
se
r
f
re
q
u
e
n
t
p
a
tt
e
rn
m
in
in
g
,
”
Kn
o
wled
g
e
a
n
d
In
fo
rm
a
t
io
n
S
y
ste
ms
,
v
o
l.
5
1
,
p
p
.
1
5
9
-
1
8
6
,
2
0
1
7
.
[1
1
]
Ch
e
n
,
e
t
a
l
.
,
“
S
tu
d
y
o
n
sim
il
a
rit
y
b
a
se
d
o
n
c
o
n
n
e
c
ti
o
n
d
e
g
re
e
in
s
o
c
ial
n
e
tw
o
rk
,
”
Clu
ste
r
Co
mp
u
t
i
n
g
,
v
o
l.
2
0
,
p
p
.
167
-
1
7
8
,
2
0
1
7
.
[1
2
]
P
a
rim
a
la
,
e
t
a
l
.
,
“
K
-
Ne
ig
h
b
o
u
rh
o
o
d
S
tr
u
c
tu
ra
l
S
im
il
a
rit
y
A
p
p
ro
a
c
h
f
o
r
S
p
a
ti
a
l
Clu
ste
rin
g
,
”
I
n
d
ia
n
J
o
u
r
n
a
l
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
8
,
2
0
1
5
.
[1
3
]
T
ian
,
e
t
a
l
.
,
“
Eff
icie
n
t
a
g
g
re
g
a
ti
o
n
f
o
r
g
ra
p
h
su
m
m
a
riz
a
ti
o
n
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
2
0
0
8
A
CM
S
IGM
OD
in
ter
n
a
t
io
n
a
l
c
o
n
fer
e
n
c
e
o
n
M
a
n
a
g
e
me
n
t
o
f
d
a
t
a
-
S
IGM
OD
'0
8
,
p
p
.
5
6
7
-
5
8
0
,
2
0
0
8
.
[1
4
]
Ya
m
a
z
a
k
i
,
e
t
a
l
.
,
“
W
e
ig
h
ted
M
icr
o
-
Clu
ste
ri
n
g
:
A
p
p
li
c
a
ti
o
n
to
Co
m
m
u
n
it
y
De
tec
ti
o
n
in
L
a
rg
e
-
S
c
a
le
Co
-
P
u
rc
h
a
sin
g
Ne
tw
o
rk
s
w
it
h
Us
e
r
A
tt
rib
u
tes
,
”
Pro
c
e
e
d
in
g
s
o
f
th
e
2
5
t
h
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
Co
mp
a
n
i
o
n
o
n
W
o
rl
d
W
id
e
W
e
b
,
p
p
.
1
3
1
-
1
3
2
,
2
0
1
6
.
[1
5
]
Ne
wm
a
n
M
.
E.
a
n
d
G
irv
a
n
M
.
,
“
F
in
d
in
g
a
n
d
e
v
a
lu
a
ti
n
g
c
o
m
m
u
n
it
y
stru
c
tu
re
in
n
e
tw
o
rk
s,
”
Ph
y
sic
a
l
Rev
iew
E
,
v
o
l
.
6
9
,
p
p
.
0
2
6
1
1
3
,
2
0
0
4
.
[1
6
]
Ba
i
,
e
t
a
l
.
,
“
F
a
st
g
ra
p
h
c
l
u
ste
rin
g
w
it
h
a
n
e
w
d
e
sc
rip
ti
o
n
m
o
d
e
l
f
o
r
c
o
m
m
u
n
it
y
d
e
tec
ti
o
n
,
”
I
n
f
o
rm
a
ti
o
n
S
c
ien
c
e
s
,
v
o
l.
3
8
8
-
3
8
9
,
p
p
.
3
7
-
4
7
,
2
0
1
7
.
[1
7
]
M
a
n
d
a
l
J.
K.
a
n
d
R
o
y
P
.
,
“
A
n
o
v
e
l
sp
e
c
tral
c
lu
ste
ri
n
g
b
a
se
d
o
n
l
o
c
a
l
d
istri
b
u
ti
o
n
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
5
,
p
p
.
3
6
1
,
2
0
1
5
.
[1
8
]
J
.
S
u
p
a
k
p
o
n
g
a
n
d
H
.
C
h
o
o
c
h
a
rt,
“
G
r
a
p
h
-
Ba
se
d
Co
n
c
e
p
t
Cl
u
ste
rin
g
f
o
r
W
e
b
S
e
a
rc
h
Re
su
lt
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
5
,
p
p
.
1
5
3
6
-
1
5
4
4
,
2
0
1
5
.
[1
9
]
F
.
S
a
n
t
o
,
“
C
o
m
m
u
n
it
y
d
e
tec
ti
o
n
in
g
ra
p
h
s,
”
P
h
y
sic
s R
e
p
o
rts
,
v
o
l
.
4
8
6
,
p
p
.
7
5
-
1
7
4
,
2
0
1
0
.
[2
0
]
A
d
a
m
ic
,
e
t
a
l
.
,
“
T
h
e
p
o
li
ti
c
a
l
b
l
o
g
o
sp
h
e
re
a
n
d
t
h
e
2
0
0
4
US
e
lec
ti
o
n
:
d
iv
id
e
d
th
e
y
b
lo
g
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
3
r
d
in
ter
n
a
t
io
n
a
l
wo
rk
sh
o
p
o
n
L
in
k
d
isc
o
v
e
ry
,
p
p
.
3
6
-
4
3
,
2
0
0
5
.
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