I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
14
, N
o.
3
,
J
une
2025
, pp.
1752
~
1762
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
3
.pp
1752
-
1762
1752
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
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s
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ap
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s
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ah
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r
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al
ah
1
,
E
s
r
aa A
b
u
E
ls
ou
d
2
,
K
am
al
S
al
ah
3
1
D
e
pa
r
t
m
e
nt
of
I
nf
or
m
a
t
i
on
T
e
c
hnol
ogy,
F
a
c
ul
t
y of
P
r
i
nc
e
A
l
-
H
us
s
e
i
n bi
n A
bd
ul
l
a
h I
I
f
or
I
nf
or
m
a
t
i
on T
e
c
hnol
ogy,
T
he
H
a
s
he
m
i
t
e
U
ni
ve
r
s
i
t
y
, Z
a
r
qa
, J
or
da
n
2
D
e
pa
r
t
m
e
nt
of
C
om
put
e
r
S
c
i
e
nc
e
, F
a
c
ul
t
y of
I
nf
or
m
a
t
i
on
T
e
c
hnol
ogy, Z
a
r
qa
U
ni
ve
r
s
i
t
y, Z
a
r
qa
, J
or
da
n
3
D
e
a
ns
hi
p of
P
r
e
pa
r
a
t
or
y Y
e
a
r
a
nd
S
uppor
t
i
ng S
t
udi
e
s
,
I
m
a
m
A
bdul
r
a
hm
a
n B
i
n F
a
i
s
a
l
U
ni
ve
r
s
i
t
y, D
a
m
m
a
m
, S
a
udi
A
r
a
bi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
S
e
p
18
,
2024
R
e
vi
s
e
d
N
ov
18
,
2024
A
c
c
e
pt
e
d
N
ov
24
,
2024
Understanding
how
patient
demographics
and
sha
red
experiences
impact
interactio
ns
is
essential
for
strengthenin
g
pa
/
tient
support
networ
ks
and
optimizing
health
outcomes
as
personalized
healthcare
becomes
mo
re
and
more
important.
To
this
end,
this
study
explores
the
patient
-
patient
interactio
ns
(PPIs)
graph
as
a
network
and
applies
selected
network
a
nalysis
approaches
to
examine
the
PPIs
network
of
accutane
drug
.
Two
main
research
questions
are
addressed
by
gaining
deeper
insight
at
the
hidden
patterns
of
reactivity
and
connectivity
among
interchanging
nodes.
There
was
a
negative
response
to
the
first
research
question,
which
as
ked
if
patients
react
to
others
that
have
similar
gender
and/or
age
profile
s
in
a
consist
ent
way.
Patient
s
tended
to
interact
with
people
o
f
different
g
enders
and
ages,
indicating
a
high
degree
of
heterogeneity
in
the
network.
N
egative
responses
were
likewise
given
to
the
second
research
question,
which
asked
if
communities
inside
the
n
etwork
could
identify
patients
based
on
ge
nder
or
age
profile.
Network
analysis
approaches
for
community
det
ection
fa
iled
to
distinguish between groups with similar demogr
aphic charact
eristics
.
Rather,
groups
seemed
to
emerge
based
on
other
factors,
like
similarity
in
patient
opinions.
The
results
imply
that
gender
and
age
do
not
have
a
major
influence
on
community
membership.
Future
resear
ch
will
concentr
ate
on
applyin
g
more
sophis
ticated
graph
mining
techniqu
es
to
expand
these
approaches t
o cover mo
re and larg
er PPIs
networks.
K
e
y
w
o
r
d
s
:
A
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
G
r
a
ph ne
twor
k a
na
ly
s
is
M
e
di
c
a
l
da
ta
a
na
ly
s
is
N
a
tu
r
a
l
la
ngua
ge
pr
oc
e
s
s
in
g
O
pi
ni
on mi
ni
ng
T
e
xt
m
in
in
g
T
e
xt
vi
s
ua
li
z
a
ti
on
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
Z
a
he
r
S
a
la
h
D
e
pa
r
tm
e
nt
of
I
nf
or
m
a
ti
on T
e
c
hnol
ogy
F
a
c
ul
ty
of
P
r
in
c
e
A
l
-
H
us
s
e
in
bi
n A
bdul
la
h I
I
f
or
I
n
f
or
m
a
ti
on T
e
c
hnol
ogy,
T
he
H
a
s
he
m
it
e
U
ni
ve
r
s
it
y
P
.O
. B
ox 330127, Z
a
r
qa
13133,
J
or
da
n
E
m
a
il
:
z
a
he
r
@
hu.e
du.j
o
1.
I
N
T
R
O
D
U
C
T
I
O
N
A
ne
w
he
te
r
oge
ne
ou
s
ne
twor
k
e
m
be
ddi
ng
te
c
hni
que
c
a
ll
e
d
s
e
lf
-
da
ta
he
te
r
oge
ne
ous
in
f
or
m
a
ti
on
ne
twor
k
e
m
be
ddi
ng
(
S
D
H
I
N
E
)
,
w
hi
c
h
in
c
or
por
a
te
s
pa
ti
e
nt
-
pa
ti
e
nt
in
te
r
a
c
ti
ons
(
PPI
s)
da
ta
in
to
dr
ug
e
m
be
ddi
ngs
a
nd
is
a
ppl
ic
a
bl
e
to
va
r
io
us
ki
nds
of
a
dve
r
s
e
dr
ug
r
e
a
c
ti
on
(
A
D
R
)
pr
e
di
c
ti
on
ta
s
ks
,
w
a
s
de
s
c
r
ib
e
d
by
B
a
of
a
ng
e
t
al
.
[
1]
.
T
he
a
ut
hor
s
f
ir
s
t
de
s
ig
n
e
d
va
r
io
us
m
e
ta
-
pa
th
-
ba
s
e
d
pr
oxi
m
it
ie
s
to
c
a
lc
ul
a
te
dr
ug
s
im
il
a
r
it
ie
s
,
pa
r
ti
c
ul
a
r
ly
ta
r
ge
t
pr
opa
ga
ti
on
m
e
ta
-
pa
th
-
ba
s
e
d
pr
oxi
m
it
y
ba
s
e
d
on
P
P
I
ne
twor
k,
a
nd
th
e
n
bui
lt
a
s
e
m
i
-
s
upe
r
vi
s
e
d s
ta
c
ki
ng de
e
p ne
ur
a
l
ne
twor
k m
ode
l
th
a
t
is
j
oi
nt
ly
i
m
p
r
ove
d by the
de
f
in
e
d m
e
ta
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pa
th
pr
oxi
m
it
ie
s
in
or
de
r
to
in
te
gr
a
te
m
ix
e
d
dr
ug
in
f
or
m
a
ti
on
a
nd
l
e
a
r
n
dr
ug
r
e
pr
e
s
e
nt
a
ti
ons
.
T
he
e
f
f
ic
a
c
y
of
th
e
S
D
H
I
N
E
m
ode
l
is
pr
ove
n
by
c
om
p
r
e
he
ns
iv
e
e
va
lu
a
ti
ons
on
th
r
e
e
A
D
R
pr
e
di
c
ti
on
ta
s
ks
us
in
g
th
r
e
e
m
ode
r
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
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8938
E
x
pl
or
in
g pati
e
nt
-
pat
ie
nt
i
nt
e
r
ac
ti
ons
gr
aphs
b
y
ne
tw
or
k
analy
s
i
s
(
Z
ahe
r
Sal
ah
)
1753
ne
twor
k e
m
be
ddi
ng t
e
c
hni
que
s
. A
ddi
ti
ona
ll
y, by ma
ppi
ng t
he
dr
ug r
e
pr
e
s
e
nt
a
ti
ons
i
nt
o 2
D
s
im
pl
e
r
s
pa
c
e
, t
he
a
ut
hor
s
c
om
pa
r
e
d
th
e
dr
ug
r
e
p
r
e
s
e
nt
a
ti
ons
in
te
r
m
s
of
dr
ug
di
s
c
r
im
in
a
ti
on.
T
he
r
e
s
ul
ts
de
m
ons
tr
a
te
d
th
a
t
th
e
pr
opos
e
d
te
c
hni
que
pe
r
f
or
m
e
d
be
tt
e
r
th
a
n
th
e
c
om
pa
r
a
ti
ve
m
e
th
ods
.
Z
ha
o
e
t
al
.
[
2
]
us
e
d
th
e
ne
twor
k
e
m
be
ddi
ng
te
c
hni
que
known
a
s
M
a
s
hup
in
th
e
ir
r
e
s
e
a
r
c
h
to
e
xt
r
a
c
t
im
por
ta
nt
a
nd
in
f
or
m
a
ti
ve
dr
ug
f
e
a
tu
r
e
s
f
r
om
a
numbe
r
of
d
r
ug he
te
r
oge
ne
ous
ne
twor
ks
t
ha
t
r
e
pr
e
s
e
nt
e
d va
r
io
us
pha
r
m
a
c
ol
ogi
c
a
l
f
e
a
tu
r
e
s
. I
n or
de
r
t
o
e
xt
r
a
c
t
s
id
e
e
f
f
e
c
t
f
e
a
tu
r
e
s
,
a
n
e
twor
k
w
a
s
a
ls
o
c
ons
tr
uc
te
d
f
o
r
s
id
e
e
f
f
e
c
ts
.
T
he
s
e
f
unc
ti
ons
a
r
e
c
a
pa
bl
e
of
ga
th
e
r
in
g
c
r
uc
ia
l
da
ta
a
t
th
e
ne
twor
k
le
ve
l
on
dr
ugs
a
nd
th
e
ir
a
dve
r
s
e
s
id
e
e
f
f
e
c
ts
.
E
a
c
h
p
a
ir
of
dr
ug
a
nd
s
id
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f
f
e
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t
w
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pr
e
s
e
nt
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om
bi
ni
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a
s
pe
c
t
s
of
th
e
dr
ug
a
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t
he
a
dve
r
s
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e
f
f
e
c
t.
M
or
e
ove
r
,
th
e
y
w
e
r
e
in
put
in
to
th
e
r
a
ndom
f
or
e
s
t
(
R
F
)
ne
twor
k
m
ode
l,
a
pr
e
di
c
ti
on
m
ode
l
c
r
e
a
te
d
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th
e
R
F
a
lg
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hm
.
F
ol
lo
w
in
g
s
e
ve
r
a
l
r
ounds
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s
ts
,
th
e
a
ve
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a
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M
a
tt
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c
or
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e
la
ti
on
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oe
f
f
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ie
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s
f
or
th
e
ba
la
nc
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d
a
nd
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la
nc
e
d
da
ta
s
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ts
w
e
r
e
f
ound
to
be
0.640
a
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0.641,
r
e
s
p
e
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ti
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ly
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c
c
or
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e
xp
e
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im
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l
r
e
s
ul
ts
e
va
lu
a
ti
ng
th
e
R
F
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twor
k
m
ode
l.
C
om
pa
r
e
d
to
e
a
r
li
e
r
m
ode
ls
u
s
in
g
ot
he
r
m
a
c
hi
ne
le
a
r
ni
ng
a
lg
or
it
hm
s
,
th
e
R
F
ne
twor
k
m
ode
l
pe
r
f
or
m
e
d be
tt
e
r
.
A
ne
w
a
ppr
oa
c
h
to
pr
e
di
c
ti
ng
pos
s
ib
le
dr
ug
s
id
e
e
f
f
e
c
ts
w
a
s
e
s
ta
bl
i
s
he
d
in
th
e
r
e
s
e
a
r
c
h
w
or
k
de
s
c
r
ib
e
d
in
[
3]
.
T
hi
s
a
ppr
oa
c
h
is
ba
s
e
d
on
m
or
e
c
om
pl
e
te
in
f
or
m
a
ti
on
a
bout
dr
ugs
th
a
t
in
te
gr
a
te
s
th
e
dr
ug
’
s
f
or
m
s
of
e
f
f
e
c
t
on
pr
ot
e
in
s
of
in
te
r
e
s
t.
A
c
e
r
ti
f
ie
d
he
te
r
oge
ne
ous
in
f
or
m
a
ti
on
ne
twor
k
is
u
s
e
d
to
m
ode
l
s
e
ve
r
a
l
s
or
ts
of
dr
ug
in
f
or
m
a
ti
on.
U
s
in
g
two
bi
a
s
r
a
ndom
w
a
lk
m
e
th
ods
to
e
xt
r
a
c
t
dr
ug
s
e
que
nc
e
s
a
nd
tr
a
in
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s
ki
p
-
gr
a
m
m
ode
l
to
le
a
r
n
dr
ug
e
m
be
ddi
ng,
th
e
a
ut
hor
s
pr
e
s
e
nt
e
d
a
ve
r
if
ie
d
he
te
r
oge
ne
ous
in
f
or
m
a
ti
on
ne
twor
k
e
m
be
ddi
ng
f
r
a
m
e
w
or
k
f
o
r
le
a
r
ni
ng
dr
ug
e
m
be
ddi
ng
a
nd
pr
e
di
c
ti
ng
dr
ug
s
id
e
e
f
f
e
c
ts
.
B
y
c
ont
r
a
s
ti
ng
th
e
out
c
om
e
s
of
th
e
e
xpe
r
im
e
nt
s
w
it
h
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e
m
os
t
a
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nc
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d
te
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hni
que
s
,
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e
pr
opos
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m
e
th
od
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s
pe
r
f
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m
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nc
e
w
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s
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ove
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or
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ove
r
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c
a
s
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tu
dy
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s
out
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om
e
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va
li
da
te
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pot
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s
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t
ha
t
dr
ugs
e
f
f
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ts
on t
a
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ti
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Z
ha
o
[
4]
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ve
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ti
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lp
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r
m
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c
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ha
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m
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D
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R
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to
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k m
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pos
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ddi
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to
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th
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f
f
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ti
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s
s
of
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ti
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G
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L
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w
a
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nt
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d
in
[
5]
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or
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to
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r
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uni
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c
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D
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I
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ph
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da
ti
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t
G
A
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C
L
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out
pe
r
f
o
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m
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ppr
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s
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ddi
ti
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ll
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a
s
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tu
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e
s
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d t
ha
t
G
A
T
C
L
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D
i
s
a
good tool
f
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di
s
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s
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le
c
ir
c
R
N
A
s
l
in
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d t
o di
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s
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r
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m
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t
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dge
gr
a
ph f
or
pr
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c
is
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n dr
ug a
na
ly
s
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, w
a
s
pr
opos
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d by the
a
ut
hor
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i
n
[
6]
.
P
r
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K
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f
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xpa
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2.1. P
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p
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a
te
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om
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ni
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e
li
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bl
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
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N
:
2252
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8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1752
-
1762
1754
w
he
r
e
th
is
is
c
r
uc
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f
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pr
a
c
ti
c
a
li
ty
of
a
na
ly
s
e
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in
r
e
s
e
a
r
c
h
in
m
e
di
c
in
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.
W
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h
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e
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pe
c
t
to
pa
ti
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ppr
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lt
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pr
of
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(
I
H
P
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p
r
opos
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d
by
[
7]
c
a
n
be
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dopt
e
d.
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H
P
is
a
de
c
e
nt
r
a
li
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m
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ve
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ti
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h
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.
[
8]
us
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of
th
e
s
e
di
s
s
a
ti
s
f
a
c
ti
o
ns
c
oul
d
be
gr
a
phi
c
a
ll
y
vi
s
ua
li
z
e
d
by
us
in
g
s
e
nt
im
e
nt
a
na
ly
s
is
te
c
hni
que
s
to
e
xt
r
a
c
t
(
P
P
I
s
)
gr
a
phs
.
T
hi
s
a
l
lo
w
e
d
f
or
a
m
or
e
c
om
pl
e
t
e
c
om
pr
e
he
n
s
io
n
of
th
e
in
f
or
m
a
ti
on
hi
dde
n
in
a
la
r
ge
qua
nt
it
y
of
th
e
s
e
w
r
it
te
n
r
e
pr
e
s
e
nt
a
ti
ons
f
or
th
e
c
or
r
e
s
ponding
p
a
ti
e
nt
s
’
r
e
vi
e
w
s
.
T
he
pr
in
c
ip
le
be
hi
nd
th
is
a
ppr
oa
c
h
is
to
us
e
a
gr
a
ph
to
vi
s
ua
li
z
e
th
e
in
te
r
a
c
ti
ons
’
s
tr
uc
tu
r
e
,
w
it
h
pa
ti
e
nt
s
pa
r
ti
c
ip
a
ti
ng
a
s
node
s
a
nd
in
te
r
a
c
ti
ons
a
s
li
nks
.
B
a
s
e
d
on
th
e
te
xt
s
im
il
a
r
it
y
of
node
s
,
li
nks
a
r
e
c
r
e
a
te
d. S
e
nt
iW
or
dN
e
t
3.0 s
e
nt
im
e
nt
l
e
xi
c
on i
s
us
e
d t
o c
la
s
s
if
y
node
s
ba
s
e
d on the
pa
ti
e
nt
’
s
a
tt
it
ude
t
ow
a
r
d a
c
e
r
ta
in
dr
ug,
w
he
th
e
r
it
be
pos
it
iv
e
or
ne
ga
ti
ve
.
N
e
xt
,
a
tt
it
ud
e
s
a
r
e
us
e
d
to
c
la
s
s
if
y
th
e
gr
a
ph
li
nka
ge
s
a
s
e
it
he
r
in
f
a
vor
of
or
a
ga
in
s
t
dr
ug
us
e
.
I
f
th
e
two
pa
ti
e
nt
s
ha
v
e
th
e
s
a
m
e
a
tt
it
ude
th
a
t
is
,
a
n
e
ga
ti
ve
a
tt
it
ude
r
e
ga
r
di
ng
s
e
ve
r
e
s
id
e
e
f
f
e
c
ts
or
a
pos
it
iv
e
a
tt
it
ude
r
e
ga
r
di
ng
m
ode
r
a
te
s
id
e
e
f
f
e
c
ts
th
e
r
e
la
ti
ons
hi
p
is
de
e
m
e
d
s
uppor
ti
ve
;
if
not
,
it
is
de
e
m
e
d
oppos
in
g. T
he
c
ons
e
qu
e
nt
gr
a
p
hs
s
how
dr
ugs
a
s
th
e
s
ubj
e
c
t
of
a
di
s
a
gr
e
e
m
e
nt
be
twe
e
n t
w
o oppos
in
g gr
oups
.
T
he
P
P
I
s
gr
a
ph e
xt
r
a
c
ti
on me
th
odol
ogy is
i
ll
us
tr
a
te
d i
n F
ig
ur
e
1.
F
ig
ur
e
1. P
P
I
s
gr
a
ph e
xt
r
a
c
ti
on f
r
a
m
e
w
or
k
A
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n)
,
one
of
th
e
dr
ugs
f
r
om
our
D
r
ugL
ib
pa
ti
e
nt
r
e
vi
e
w
s
da
ta
s
e
t,
i
s
us
e
d
to
ge
ne
r
a
te
P
P
I
s
gr
a
phs
. T
he
r
e
s
ul
ti
ng gr
a
ph i
s
pr
e
s
e
nt
e
d i
n F
ig
ur
e
2. F
ig
ur
e
2 s
how
s
a
de
s
ig
na
te
d node
f
or
e
a
c
h
pa
ti
e
nt
,
la
be
le
d
w
it
h
th
e
pa
ti
e
nt
’
s
a
ge
(
th
e
num
be
r
be
twe
e
n
pa
r
e
nt
he
s
e
s
)
a
nd
ge
nde
r
(
F
:
f
e
m
a
le
,
M
:
m
a
le
)
.
F
r
om
th
e
pa
ti
e
nt
’
s
pe
r
s
p
e
c
ti
ve
on
th
e
dr
ug
und
e
r
c
ons
id
e
r
a
t
io
n,
a
gr
e
e
n
node
in
di
c
a
t
e
s
a
pa
ti
e
nt
w
it
h
a
pos
it
iv
e
a
tt
it
ude
(
m
ode
r
a
te
s
id
e
e
f
f
e
c
t)
a
nd
a
r
e
d
node
in
di
c
a
te
s
a
pa
ti
e
nt
w
it
h a
ne
ga
ti
ve
a
tt
it
ude
(
s
e
ve
r
e
s
id
e
e
f
f
e
c
t)
.
W
he
n
two
li
nke
d
node
s
r
e
pr
e
s
e
nt
two
pa
ti
e
nt
s
,
th
e
th
ic
kne
s
s
of
th
e
li
nk
be
twe
e
n
th
e
m
r
e
f
le
c
ts
how
s
im
il
a
r
th
e
ir
s
e
m
a
nt
ic
m
a
te
r
ia
l
is
.
T
hi
s
is
c
a
lc
ul
a
te
d
by
s
um
m
in
g
up
a
ll
th
e
phr
a
s
e
s
(
w
or
ds
)
in
bot
h
r
e
vi
e
w
s
th
a
t
ha
ve
non
-
z
e
r
o
w
e
ig
ht
s
f
or
te
r
m
f
r
e
que
nc
y
-
in
ve
r
s
e
doc
um
e
nt
f
r
e
que
nc
y
(
TF
-
I
D
F
)
a
nd
a
ppe
a
r
to
be
a
bout
th
e
s
a
m
e
is
s
ue
.
T
he
li
nks
w
it
h
gr
e
e
n
c
ol
or
s
in
di
c
a
te
pe
opl
e
w
ho
e
ndor
s
e
or
a
r
e
in
a
ppr
ova
l,
w
hi
le
th
e
li
nks
w
it
h r
e
d c
ol
or
s
i
ndi
c
a
te
t
hos
e
w
ho a
r
e
a
ga
in
s
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
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8938
E
x
pl
or
in
g pati
e
nt
-
pat
ie
nt
i
nt
e
r
ac
ti
ons
gr
aphs
b
y
ne
tw
or
k
analy
s
i
s
(
Z
ahe
r
Sal
ah
)
1755
F
ig
ur
e
2.
P
P
I
s
gr
a
ph f
o
r
a
c
c
ut
a
ne
dr
ug (
is
ot
r
e
ti
noi
n)
pr
oduc
e
d f
r
om
dr
ugl
ib
.c
om
pa
ti
e
nt
s
r
e
vi
e
w
s
T
he
a
ut
hor
s
w
il
l
us
e
th
e
in
f
or
m
a
ti
on
m
e
nt
io
ne
d
e
a
r
li
e
r
to
de
m
ons
tr
a
te
how
P
P
I
s
gr
a
phs
c
a
n
be
us
e
d
to
f
a
c
il
it
a
te
va
r
io
us
le
ve
l
s
of
a
na
ly
s
is
.
P
P
I
s
gr
a
phs
c
a
n
b
e
us
e
d,
in
m
or
e
de
ta
il
,
to
e
x
a
m
in
e
:
i)
if
pa
ti
e
nt
s
c
ons
is
te
nt
ly
r
e
s
pond
e
d
to
th
os
e
w
ho
di
d
f
it
th
e
s
a
m
e
g
e
nde
r
or
a
ge
pr
of
il
e
a
nd
ii
)
if
th
e
P
P
I
s
gr
a
ph
(
ne
twor
k)
“
c
om
m
uni
ty
”
r
e
f
le
c
t
a
ge
nd
e
r
or
a
ge
pr
of
il
e
.
T
he
c
or
e
obj
e
c
ti
v
e
of
th
e
r
e
s
e
a
r
c
h
w
or
k
pr
e
s
e
nt
e
d
in
th
is
pa
pe
r
is
to
id
e
nt
if
y
th
e
s
tr
uc
tu
r
a
l
pr
ope
r
ti
e
s
a
nd
hi
ghl
ig
ht
s
om
e
of
th
e
f
e
a
tu
r
e
s
of
th
e
gr
a
phs
,
s
uc
h
a
s
how
pa
ti
e
nt
s
a
r
e
li
ke
ly
to
r
a
nk
th
e
s
id
e
e
f
f
e
c
ts
of
dr
ugs
,
how
p
a
ti
e
nt
s
in
te
r
a
c
t
in
th
e
ir
r
e
vi
e
w
s
,
a
nd
w
hi
c
h
p
a
ti
e
nt
s
a
r
e
m
or
e
in
f
lu
e
nt
ia
l,
by
a
ppl
yi
ng
ne
twor
k
a
na
ly
s
is
te
c
hni
que
s
to
th
e
gr
a
phs
s
e
e
th
e
e
xa
m
pl
e
s
on
c
om
bi
ni
ng
s
e
nt
im
e
nt
a
na
ly
s
is
a
nd
ne
twor
ks
a
n
a
ly
s
is
pr
e
s
e
nt
e
d
in
s
tu
di
e
s
[
9]
−
[
17]
.
T
o
th
e
be
s
t
of
th
e
a
ut
hor
’
s
knowle
dge
,
no
e
a
r
li
e
r
r
e
s
e
a
r
c
h
ha
s
m
a
de
a
n
a
tt
e
m
pt
to
c
ha
r
a
c
te
r
iz
e
a
nd
a
na
ly
z
e
pa
ti
e
nt
r
e
vi
e
w
s
in
th
is
w
a
y
w
it
h
a
c
onc
e
nt
r
a
ti
on
on
s
id
e
e
f
f
e
c
ts
in
th
e
c
ont
e
xt
of
a
c
e
r
ta
in
dr
ug.
T
he
r
e
s
e
a
r
c
h
de
s
c
r
ib
e
d
he
r
e
a
im
s
to
a
na
ly
z
e
th
e
la
te
nt
gr
a
ph
s
tr
uc
tu
r
e
s
th
a
t
a
r
e
e
xi
s
te
nt
in
gr
a
phs
of
P
P
I
s
in
r
e
la
ti
on
to
th
e
in
te
r
a
c
ti
ons
be
twe
e
n
th
e
in
di
vi
dua
l
pa
ti
e
nt
s
. I
s
i
t
pos
s
ib
le
t
o u
s
e
a
ppl
ic
a
bl
e
t
e
c
hni
que
s
f
r
om
t
he
f
ie
ld
of
ne
twor
k a
na
ly
s
is
t
o r
e
pr
e
s
e
nt
a
nd a
n
a
ly
z
e
P
P
I
s
a
s
gr
a
phs
(
c
onc
e
pt
ua
li
z
e
d
a
s
ne
twor
ks
)
?
M
or
e
pr
e
c
is
e
ly
,
w
ha
t
ne
twor
k
a
na
ly
s
is
m
e
tr
ic
s
a
nd
m
e
th
ods
s
houl
d be
a
ppl
ie
d t
o dr
a
w
a
tt
e
nt
io
n t
o t
he
s
tr
uc
tu
r
a
l
c
ha
r
a
c
t
e
r
is
ti
c
s
of
t
he
s
e
ki
nds
of
gr
a
phs
?
T
he
a
ut
hor
s
w
il
l
e
xpl
a
in
a
n
a
ppr
oa
c
h
f
or
a
na
ly
z
in
g
P
P
I
s
gr
a
phs
us
in
g
n
e
twor
k
m
e
tr
ic
s
a
nd
c
om
m
uni
ty
de
te
c
ti
on
a
lg
or
it
hm
s
in
th
e
s
e
c
ti
on
th
a
t
f
ol
lo
w
s
.
T
hi
s
a
ppr
oa
c
h
is
ba
s
e
d
on
a
pi
lo
t
s
tu
dy.
T
he
im
por
ta
nc
e
of
th
is
r
e
s
e
a
r
c
h
is
ba
s
e
d
on
th
e
f
a
c
t
th
a
t
by
e
xa
m
in
in
g
e
xi
s
ti
ng
pa
tt
e
r
ns
of
c
onne
c
ti
ons
a
nd
in
vol
ve
m
e
nt
be
twe
e
n
th
e
e
xc
h
a
ngi
ng
node
s
(
pa
ti
e
nt
s
)
,
ne
tw
or
k
m
e
a
s
ur
e
m
e
nt
s
a
nd
c
om
m
uni
ty
de
te
c
ti
on
c
om
put
a
ti
ona
l
m
e
th
ods
c
a
n be
u
s
e
d t
o a
nt
ic
ip
a
te
out
c
om
e
s
.
2.2. P
P
I
s
gr
ap
h
an
al
ys
is
T
hi
s
s
e
c
ti
on
e
xpl
a
in
s
how
to
e
f
f
e
c
ti
ve
ly
m
a
ke
us
e
of
(
P
P
I
s
)
gr
a
phs
f
or
s
uppor
ti
ng
d
if
f
e
r
e
nt
ty
pe
s
of
a
na
ly
s
is
,
a
s
th
e
y
a
r
e
c
ons
tr
uc
te
d
us
in
g
th
e
(
P
P
I
s
)
gr
a
ph
e
xt
r
a
c
ti
on
f
r
a
m
e
w
or
k.
P
P
I
s
gr
a
phs
c
a
n
be
us
e
d
in
pa
r
ti
c
ul
a
r
t
o:
i
)
i
nve
s
ti
ga
te
w
he
th
e
r
pa
ti
e
nt
s
c
ons
is
te
nt
ly
r
e
s
pon
de
d t
o ot
he
r
pa
ti
e
nt
s
w
ho ha
d a
s
im
il
a
r
ge
nde
r
or
a
ge
pr
of
il
e
;
a
nd
ii
)
in
ve
s
ti
ga
te
w
h
e
th
e
r
th
e
ge
nd
e
r
or
a
ge
pr
o
f
il
e
of
th
e
“
c
om
m
uni
ty
”
in
s
id
e
th
e
P
P
I
s
gr
a
ph
(
ne
twor
k)
is
in
di
c
a
te
d.
T
he
f
ir
s
t,
it
f
oc
us
e
s
on
(
P
P
I
s
)
gr
a
phs
a
nd
di
s
c
u
s
s
e
s
th
e
na
tu
r
e
of
th
e
a
r
gum
e
nt
s
be
twe
e
n
th
e
two
pa
r
ti
e
s
a
bout
a
n
a
s
s
o
c
ia
te
d
dr
ug.
T
he
s
e
c
ond
de
a
ls
w
it
h
r
e
c
ogni
ti
on
of
c
om
m
uni
ti
e
s
w
it
hi
n
(
P
P
I
s
)
gr
a
phs
a
nd
th
e
pos
s
ib
le
in
te
r
pr
e
ta
ti
ons
of
th
e
s
e
c
om
m
uni
ti
e
s
’
c
ha
r
a
c
te
r
is
ti
c
s
.
S
in
c
e
th
e
th
e
or
y
of
ne
twor
k
a
na
ly
s
is
i
s
th
e
f
ounda
ti
on
of
bot
h
ty
pe
s
of
in
ve
s
ti
ga
ti
ons
[
18]
,
(
P
P
I
s
)
gr
a
phs
c
a
n
be
in
te
r
pr
e
te
d
a
s
ne
twor
ks
.
C
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
c
on
c
e
pt
is
r
e
c
om
m
e
nde
d
f
or
th
e
f
ir
s
t
ty
pe
of
a
na
ly
s
is
(
a
s
s
or
ta
ti
vi
ty
c
a
n
a
ls
o
be
us
e
d [
18]
)
. D
if
f
e
r
e
nt
ne
twor
k
c
om
m
uni
ty
de
te
c
ti
on c
om
put
a
ti
ona
l
m
e
th
ods
c
a
n be
s
uc
c
e
s
s
f
ul
ly
a
ppl
ie
d f
or
th
e
s
e
c
ond type
of
a
na
ly
s
is
, a
s
w
il
l
be
c
ove
r
e
d i
n m
or
e
de
ta
il
l
a
t
e
r
i
n t
hi
s
s
e
c
ti
on. W
it
h r
e
s
pe
c
t
to
t
he
i
nt
e
nde
d
(
P
P
I
s
)
gr
a
ph,
th
e
f
ol
lo
w
in
g
e
xe
m
pl
a
r
que
s
ti
ons
w
e
r
e
ta
k
e
n
i
nt
o
c
ons
id
e
r
a
ti
on
in
or
de
r
to
de
m
ons
tr
a
te
th
e
us
e
f
ul
ne
s
s
of
t
he
gr
a
ph i
n t
he
c
ont
e
xt
of
t
he
t
w
o t
ype
s
of
a
na
ly
s
is
m
e
nt
io
ne
d i
n s
e
c
ti
on 2.3:
Q
1:
A
r
e
pa
ti
e
nt
s
c
ons
i
s
te
nt
ly
r
e
s
ponding t
o pa
ti
e
nt
s
be
lo
ngi
ng t
o a
s
im
il
a
r
ge
nde
r
a
nd/
or
a
ge
pr
of
il
e
?
Q
2:
A
r
e
c
om
m
uni
ti
e
s
f
ound in t
he
(
P
P
I
s
)
ne
twor
k a
bl
e
t
o i
de
nt
i
f
y, a
t
le
a
s
t
r
oughly, a
pa
ti
e
nt
’
s
a
ge
or
ge
nde
r
?
T
he
vi
s
ua
l
i
z
a
t
i
on w
a
s
pr
oduc
e
d u
s
i
ng G
e
phi
a
t
ht
t
ps
:
/
/
ge
phi
.or
g/
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1752
-
1762
1756
2.
3
. T
h
e
ac
c
u
t
an
e
(
is
ot
r
e
t
in
oi
n
)
n
e
t
w
or
k
W
it
h
35
node
s
r
e
pr
e
s
e
nt
in
g
e
a
c
h
pa
ti
e
nt
w
ho
pa
r
ti
c
ip
a
te
d
i
n
th
e
dr
ug
r
e
vi
e
w
in
g
a
nd
86
e
dge
s
r
e
pr
e
s
e
nt
in
g
pa
ti
e
nt
in
te
r
a
c
ti
ons
,
th
e
a
c
c
ut
a
ne
dr
ug
’
s
(
P
P
I
s
)
ne
twor
k
(
undi
r
e
c
te
d
gr
a
ph)
w
a
s
c
ons
tr
uc
te
d.
F
ig
ur
e
3
s
how
s
th
e
ne
twor
k
’
s
de
gr
e
e
di
s
tr
ib
ut
io
n,
w
hi
le
T
a
bl
e
1
pr
ovi
de
s
in
f
or
m
a
ti
on
on
th
e
a
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n)
node
s
.
H
ig
hl
y
c
onne
c
te
d
node
s
a
r
e
f
e
w
e
r
in
nu
m
be
r
th
a
n
poor
ly
c
onne
c
te
d
node
s
,
a
s
is
to
be
e
xpe
c
te
d.
F
r
om
a
ne
twor
k
a
na
ly
s
is
pe
r
s
pe
c
ti
ve
,
it
m
a
ke
s
s
e
ns
e
to
in
ve
s
ti
ga
t
e
if
a
ne
twor
k
’
s
de
gr
e
e
di
s
tr
ib
ut
io
n
f
it
s
a
pow
e
r
-
la
w
di
s
tr
ib
ut
io
n.
T
he
a
c
c
ut
a
ne
ne
tw
or
k
’
s
de
gr
e
e
di
s
tr
ib
ut
io
n
is
r
ig
ht
-
s
ke
w
e
d
a
nd
r
oughly
f
ol
lo
w
s
a
pow
e
r
-
la
w
di
s
tr
ib
ut
io
n,
a
s
s
how
n
by
th
e
h
is
to
gr
a
m
of
de
gr
e
e
di
s
tr
ib
ut
io
ns
in
F
ig
ur
e
3.
S
c
a
le
-
f
r
e
e
ne
twor
ks
a
r
e
de
f
in
e
d a
s
ne
twor
k
s
ha
vi
ng de
gr
e
e
di
s
t
r
ib
ut
io
ns
t
ha
t
f
ol
lo
w
a
pow
e
r
l
a
w
[
18]
.
A
s
ubs
e
t
of
node
s
f
r
om
a
gr
a
ph
c
onn
e
c
te
d
by
a
p
a
th
is
r
e
f
e
r
r
e
d t
o
a
s
a
w
e
a
kl
y
c
onne
c
t
e
d
c
om
pone
nt
in
gr
a
ph
th
e
or
y
te
r
m
in
ol
ogy.
T
he
r
e
f
or
e
,
th
e
pr
oc
e
s
s
m
us
t
lo
c
a
te
e
ve
r
y
w
e
e
kl
y
c
onne
c
t
e
d
c
om
pone
nt
of
th
e
ne
twor
k
in
or
de
r
to
obt
a
in
th
e
li
s
t
of
node
s
th
a
t
a
r
e
p
a
r
t
of
th
e
s
a
m
e
c
lu
s
te
r
or
gr
oup
of
ove
r
la
ppi
ng
c
lu
s
te
r
s
.
T
hi
s
pr
oc
e
s
s
i
s
c
a
r
r
ie
d
out
a
s
a
de
pt
h
-
f
ir
s
t
s
e
a
r
c
h,
w
hi
c
h
in
ve
s
ti
ga
te
s
a
gr
a
ph
in
it
s
e
nt
ir
e
f
or
m
,
di
ggi
ng
a
s
f
a
r
a
s
pos
s
ib
le
in
to
e
a
c
h
of
it
s
br
a
nc
he
s
be
f
or
e
ba
c
kt
r
a
c
ki
ng.
W
it
h
V
r
e
pr
e
s
e
nt
in
g
th
e
num
be
r
of
ve
r
ti
c
e
s
or
node
s
a
nd
E
r
e
pr
e
s
e
nt
in
g t
he
numbe
r
of
e
dge
s
i
n t
he
gr
a
ph, i
ts
t
im
e
c
om
pl
e
xi
ty
i
s
O
(
∣
V
∣
+
∣
E
∣
)
. T
he
node
s
a
nd
e
dge
s
f
or
e
a
c
h
w
e
a
kl
y
c
onne
c
te
d
c
om
pone
nt
a
r
e
a
c
qui
r
e
d
by
vi
s
it
in
g
e
ve
r
y
ve
r
te
x
in
th
e
gr
a
ph
[
19]
.
R
e
c
ons
tr
uc
te
d
c
lu
s
te
r
s
c
ons
i
s
t
onl
y
of
c
onne
c
te
d
c
om
pone
nt
s
th
a
t
ha
ve
m
or
e
th
a
n
one
node
.
A
c
onne
c
te
d
c
om
pone
nt
in
s
ta
ti
c
gr
a
ph
s
is
th
e
l
a
r
ge
s
t
po
s
s
ib
le
s
e
t
of
ve
r
ti
c
e
s
c
onne
c
te
d
by
gr
a
ph
e
dg
e
s
.
I
n
s
im
pl
e
r
te
r
m
s
,
if
th
e
r
e
is
a
pa
th
in
th
e
g
r
a
ph
c
onne
c
ti
ng
two
ve
r
ti
c
e
s
,
u
a
nd
v,
in
th
e
c
om
pone
nt
,
th
e
n
it
e
xi
s
ts
.
S
tr
ongl
y
a
nd
w
e
a
kl
y
li
nke
d c
om
pone
nt
s
c
a
n
be
us
e
d
to
e
xp
a
nd
th
e
c
onc
e
pt
o
f
di
r
e
c
te
d
gr
a
phs
in
two
di
f
f
e
r
e
nt
w
a
y
s
:
e
it
he
r
th
e
r
e
i
s
a
di
r
e
c
te
d pa
th
f
r
om
u t
o v a
nd one
f
r
om
v t
o u, or
onl
y
one
of
t
hos
e
pa
th
s
e
xi
s
ts
[
20]
.
F
ig
ur
e
3. T
he
a
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n)
gr
a
ph a
ve
r
a
ge
w
e
ig
ht
e
d d
e
gr
e
e
:
1.691
T
a
bl
e
1.
T
he
a
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n)
node
s
i
nf
or
m
a
ti
on
G
r
a
ph
e
l
e
m
e
nt
s
S
t
a
t
i
s
t
i
c
a
l
s
um
m
a
r
y
N
ode
s
35
E
dge
s
86
A
ve
r
a
ge
de
gr
e
e
4.914
A
ve
r
a
ge
w
e
i
ght
e
d de
gr
e
e
1.691
N
e
t
w
or
k
di
a
m
e
t
e
r
4
G
r
a
ph
de
ns
i
t
y
0.145
m
odul
a
r
i
t
y
0.638
A
ve
r
a
ge
c
l
us
t
e
r
i
ng c
oe
f
f
i
c
i
e
nt
0.867
A
ve
r
a
ge
pa
t
h l
e
ngt
h
1.398
◼
M
ode
r
a
t
e
s
i
de
-
e
f
f
e
c
t
s
84.88%
◼
S
e
ve
r
e
s
i
de
-
e
f
f
e
c
t
s
15.12%
C
onne
c
t
e
d
c
om
pone
nt
s
6
C
om
pne
nt
-
1
34.29%
C
om
pne
nt
-
2
25.71%
C
om
pne
nt
-
3
20%
C
om
pne
nt
-
4
8.57%
C
om
pne
nt
-
5
5.71%
C
om
pne
nt
-
6
5.71%
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
E
x
pl
or
in
g pati
e
nt
-
pat
ie
nt
i
nt
e
r
ac
ti
ons
gr
aphs
b
y
ne
tw
or
k
analy
s
i
s
(
Z
ahe
r
Sal
ah
)
1757
2.
4
. A
n
al
ys
is
of
ac
c
u
t
an
e
n
e
t
w
or
k
I
n
th
is
r
e
s
e
a
r
c
h,
P
P
I
s
gr
a
phs
a
r
e
e
xa
m
in
e
d
u
s
in
g
two
f
or
m
s
of
ne
twor
k
a
na
ly
s
is
.
T
o a
ns
w
e
r
r
e
s
e
a
r
c
h
que
s
ti
on
Q
1,
th
e
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
is
f
i
r
s
t
us
e
d.
S
e
c
ond,
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
is
us
e
d
f
or
c
om
m
uni
ty
s
tr
uc
tu
r
e
s
de
te
c
ti
on f
or
a
ns
w
e
r
in
g r
e
s
e
a
r
c
h que
s
ti
on Q
2.
2.
4
.1. C
lu
s
t
e
r
in
g c
oe
f
f
ic
ie
n
t
A
m
e
a
s
ur
e
of
how
m
u
c
h
node
s
in
a
gr
a
ph
te
nd
to
c
lu
s
te
r
to
g
e
th
e
r
is
c
a
ll
e
d
a
c
lu
s
t
e
r
in
g
c
oe
f
f
ic
ie
nt
in
th
e
c
ont
e
xt
of
gr
a
ph
t
he
or
y. I
t
m
e
a
s
ur
e
s
t
he
de
gr
e
e
of
c
ohe
s
io
n
in
a
node
’
s
ne
ig
hbor
hood withi
n
a
ne
twor
k.
I
t
is
c
la
s
s
if
ie
d
in
to
two
c
a
te
gor
ie
s
:
lo
c
a
l
va
lu
e
s
,
w
hi
c
h
qu
a
nt
if
y
th
e
c
ohe
s
io
n s
ur
r
ounding
a
pa
r
ti
c
ul
a
r
node
,
a
nd
gl
oba
l
va
lu
e
s
,
w
hi
c
h
qu
a
nt
if
y
th
e
c
lu
s
t
e
r
s
w
it
hi
n
th
e
ne
twor
k
a
s
a
w
hol
e
.
I
t
s
houl
d
be
unde
r
li
ne
d
th
a
t
onl
y
s
in
gl
e
-
e
dge
gr
a
phs
c
a
n u
s
e
bot
h of
t
he
c
lu
s
te
r
in
g c
oe
f
f
ic
ie
nt
’
s
f
or
m
ul
a
ti
ons
. A
ddi
ti
ona
ll
y, m
a
ny e
dge
s
a
r
e
not
ta
ke
n i
nt
o c
ons
id
e
r
a
ti
on i
n t
he
m
a
jo
r
it
y
of
m
e
a
s
ur
e
m
e
nt
s
i
n r
e
a
l
-
w
or
ld
ne
twor
ks
. T
he
y only t
a
ke
i
nt
o a
c
c
ount
ba
s
ic
gr
a
phs
f
r
e
e
of
lo
ops
a
nd
m
ul
ti
pl
e
e
dge
s
a
s
a
r
e
s
ul
t.
F
or
w
e
ig
ht
e
d
gr
a
phs
,
th
e
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
c
a
n
a
ls
o be
de
r
iv
e
d [
21]
−
[
23]
. T
he
r
a
ti
o of
e
dge
s
ne
ig
hbor
in
g node
s
t
o a
ll
pot
e
nt
ia
l
e
dge
s
be
twe
e
n t
he
m
i
s
known
a
s
th
e
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
.
A
n
a
v
e
r
a
ge
m
e
a
s
ur
e
m
e
nt
of
node
c
lu
s
te
r
in
g
in
a
ne
twor
k
is
gi
ve
n
by
th
e
gl
oba
l
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
.
S
tr
onge
r
node
te
nde
nc
y
to
f
or
m
de
ns
e
ly
c
onne
c
te
d
c
lu
s
te
r
s
is
in
di
c
a
te
d
by
hi
ghe
r
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
s
.
P
r
io
r
s
tu
di
e
s
ha
ve
de
m
on
s
tr
a
te
d
th
a
t
ne
twor
ks
w
it
h
r
a
ndom
a
nd
s
c
a
le
-
f
r
e
e
c
ha
r
a
c
te
r
is
ti
c
s
ty
pi
c
a
ll
y
ha
ve
poor
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
s
.
O
n
t
he
ot
he
r
ha
nd,
ne
twor
ks
w
it
h
la
r
ge
r
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
s
ha
ve
pr
ove
n t
o e
xhi
bi
t
a
hi
ghe
r
l
e
ve
l
of
c
or
r
e
la
ti
on [
24]
.
T
he
pr
oba
bi
li
ty
th
a
t
a
ny
two
r
a
ndoml
y
s
e
le
c
te
d
ne
ig
hbor
s
of
a
ve
r
te
x
v
,
of
de
gr
e
e
a
t
le
a
s
t
2,
a
r
e
li
nke
d
to
ge
th
e
r
is
known
a
s
th
e
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
o
f
v
.
I
f
d(
v
)
r
e
pr
e
s
e
nt
s
th
e
num
be
r
of
ne
ig
hbor
s
of
v
,
th
e
n
th
e
c
a
lc
ul
a
ti
on
is
(
(
)
2
)
=
num
be
r
of
tr
ia
ngl
e
s
c
ont
a
in
in
g
v
di
vi
de
d
by
num
be
r
of
pot
e
nt
ia
l
e
dge
s
be
twe
e
n
it
s
ne
ig
hbor
s
.
T
he
a
v
e
r
a
ge
of
th
is
va
lu
e
f
or
a
ll
ve
r
ti
c
e
s
of
de
gr
e
e
a
t
le
a
s
t
2
in
th
e
gr
a
ph
m
a
y
th
e
n
be
us
e
d
to
de
f
in
e
th
e
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
of
th
e
e
nt
i
r
e
gr
a
ph
[
2
5]
.
F
ig
ur
e
4
s
how
s
th
e
a
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n
)
gr
a
ph
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
m
e
tr
ic
r
e
por
t
(
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
di
s
tr
ib
ut
io
n)
:
pa
r
a
m
e
te
r
s
:
ne
twor
k
in
te
r
pr
e
ta
ti
on:
undi
r
e
c
te
d,
r
e
s
ul
ts
:a
ve
r
a
ge
c
lu
s
t
e
r
in
g
c
oe
f
f
ic
ie
nt
:
0.867,
to
ta
l
tr
ia
ngl
e
s
:
130,
th
e
a
ve
r
a
ge
c
lu
s
te
r
in
g c
oe
f
f
ic
ie
nt
i
s
t
he
m
e
a
n va
lu
e
of
i
ndi
vi
dua
l
c
oe
f
f
ic
ie
n
ts
.
F
ig
ur
e
4.
T
he
a
c
c
ut
a
ne
(
is
ot
r
e
ti
noi
n
)
gr
a
ph
c
lu
s
te
r
in
g c
oe
f
f
ic
ie
nt
m
e
tr
ic
r
e
por
t
2.
4
.2. C
om
m
u
n
it
y s
t
r
u
c
t
u
r
e
s
d
e
t
e
c
t
io
n
I
n
s
oc
ia
l
ne
twor
k
a
na
ly
s
is
,
th
e
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
in
de
x
is
c
r
uc
ia
l,
a
lt
hough
it
is
e
xpe
ns
iv
e
to
c
a
lc
ul
a
te
.
T
he
le
a
s
t
ti
m
e
-
c
ons
um
in
g
m
e
th
ods
a
va
il
a
bl
e
now
ta
ke
O
(
n
2
)
s
pa
c
e
a
nd
O
(
n
3
)
ti
m
e
,
w
he
r
e
n
is
th
e
num
be
r
of
node
s
in
th
e
ne
twor
k.
T
he
in
c
r
e
a
s
in
g
de
m
a
nd
f
or
c
e
nt
r
a
li
ty
m
e
a
s
ur
e
s
on
s
pa
r
s
e
,
la
r
ge
-
s
c
a
le
ne
twor
ks
ha
s
le
d
to
th
e
in
tr
oduc
ti
on
of
ne
w
be
twe
e
nne
s
s
a
lg
o
r
it
hm
s
in
[
26]
.
F
or
unw
e
ig
ht
e
d
a
nd
w
e
ig
ht
e
d
ne
twor
ks
,
r
e
s
pe
c
ti
ve
ly
,
th
e
y
ta
ke
up
O
(
n+m
)
s
pa
c
e
a
nd
e
xe
c
ut
e
in
O
(
nm
)
a
nd
O
(
nm
+n
2
lo
g
n)
ti
m
e
c
om
pl
e
xi
ty
,
w
he
r
e
m
is
th
e
num
b
e
r
of
li
nks
.
T
hi
s
s
ig
ni
f
ic
a
nt
ly
br
oa
de
ns
th
e
va
r
ie
ty
of
ne
twor
ks
f
or
w
hi
c
h
c
e
nt
r
a
li
ty
a
na
ly
s
is
is
pr
a
c
ti
c
a
l,
a
s
de
m
ons
tr
a
te
d
by
e
xp
e
r
im
e
nt
a
l
da
ta
.
C
e
nt
r
a
li
ty
in
di
c
e
s
f
or
m
e
d
on
gr
a
ph
ve
r
ti
c
e
s
a
r
e
a
c
r
uc
ia
l
to
ol
f
or
s
oc
ia
l
n
e
twor
k
a
na
ly
s
i
s
.
T
h
e
y
a
r
e
i
nt
e
nde
d
to
r
e
pr
e
s
e
nt
th
e
im
por
ta
nc
e
of
node
s
ta
ngl
e
d
in
a
s
oc
ia
l
s
tr
uc
tu
r
e
a
nd
a
r
e
us
e
d
to
r
a
nk
th
e
node
s
b
a
s
e
d
on
w
he
r
e
th
e
y
a
r
e
in
th
e
ne
twor
k.
V
a
r
io
us
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1752
-
1762
1758
c
e
nt
r
a
li
ty
in
di
c
e
s
,
s
uc
h
a
s
th
os
e
th
a
t
m
e
a
s
ur
e
a
node
’
s
a
ve
r
a
ge
di
s
ta
nc
e
f
r
om
ot
he
r
node
s
or
th
e
r
a
ti
o
o
f
s
hor
te
s
t
pa
th
s
t
ha
t
a
nod
e
l
ie
s
on, a
r
e
b
a
s
e
d on the
s
hor
te
s
t
pa
th
s
t
ha
t
li
nk pa
ir
s
of
node
s
.
A
n a
s
s
e
s
s
m
e
nt
of
t
he
s
e
i
ndi
c
e
s
i
s
a
f
unda
m
e
nt
a
l
c
om
pone
nt
of
m
a
ny ne
twor
k
-
a
na
ly
ti
c
r
e
s
e
a
r
c
h [
26]
.
A
ne
twor
k
node
’
s
im
por
ta
nc
e
is
m
e
a
s
ur
e
d
by
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
[
24]
,
[
27]
,
w
hi
c
h
is
ba
s
e
d
on
s
hor
te
s
t
pa
th
s
a
nd
r
e
f
le
c
ts
node
s
’
c
ont
r
ib
ut
io
ns
to
s
tr
uc
tu
r
a
l
s
ta
bi
li
ty
,
s
oc
ia
l
in
f
lu
e
nc
e
,
a
nd
in
f
or
m
a
ti
on
di
f
f
us
io
n.
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
is
f
r
e
que
nt
ly
us
e
d
a
c
r
os
s
va
r
io
us
dom
a
i
ns
,
in
c
lu
di
ng
in
f
lu
e
nc
e
e
va
lu
a
ti
on,
c
om
m
uni
ty
di
s
c
ove
r
y,
a
nd
s
oc
ia
l
ne
twor
k
a
na
ly
s
is
.
T
he
B
r
a
nde
’
s
a
lg
or
it
hm
[
26]
is
th
e
m
os
t
e
f
f
e
c
ti
ve
a
lg
or
it
hm
f
or
c
a
lc
ul
a
ti
ng
be
twe
e
nn
e
s
s
c
e
nt
r
a
li
ty
qui
c
kl
y.
I
t
is
ba
s
e
d
on
th
e
obs
e
r
va
ti
on
th
a
t
th
e
b
e
twe
e
nne
s
s
c
e
nt
r
a
li
ty
va
lu
e
of
a
node
v
is
e
qua
l
to
th
e
to
ta
l
of
a
ll
th
e
f
r
a
c
ti
ons
of
s
ho
r
te
s
t
pa
th
s
f
r
om
ot
he
r
node
pa
ir
s
(
s
t)
th
a
t
pa
s
s
th
r
ough
node
v
.
U
s
in
g
th
is
f
or
m
ul
a
a
s
a
s
ta
r
ti
ng
poi
nt
,
th
e
B
r
a
nde
’
s
a
lg
or
it
hm
di
s
c
ove
r
s
th
e
s
hor
te
s
t
pa
th
s
be
twe
e
n
e
a
c
h
node
v
a
nd
e
ve
r
y
ot
he
r
node
,
doc
um
e
nt
in
g
th
e
f
r
e
que
nc
y
a
nd
num
be
r
of
e
a
c
h
nod
e
a
lo
ng
th
e
s
hor
te
s
t
pa
th
s
.
T
h
e
be
twe
e
nn
e
s
s
c
e
nt
r
a
li
ty
va
lu
e
s
of
e
a
c
h
node
,
s
ta
r
ti
ng
w
it
h
th
e
le
a
f
node
s
a
nd
e
ndi
ng
a
t
th
e
r
oot
node
,
a
r
e
th
e
n
s
um
m
e
d
up
b
a
s
e
d
on
th
e
in
f
or
m
a
ti
on
ga
th
e
r
e
d.
W
he
n
c
a
lc
ul
a
ti
ng
th
e
b
e
twe
e
nne
s
s
c
e
nt
r
a
li
ty
of
e
ve
r
y
node
in
a
n
unw
e
ig
ht
e
d
gr
a
ph,
th
e
B
r
a
nde
’
s
a
lg
or
it
hm
ne
e
ds
O
(
nm
)
ti
m
e
c
om
pl
e
xi
ty
,
w
he
r
e
n
is
th
e
num
be
r
of
node
s
in
th
e
ne
twor
k
a
nd
m
is
th
e
num
be
r
of
e
dge
s
.
T
he
B
r
a
nde
’
s
a
lg
or
it
hm
ope
r
a
te
s
w
it
h
a
n
O
(
nm
+n
2
lo
gn)
ti
m
e
c
om
pl
e
xi
ty
f
or
w
e
ig
ht
e
d
gr
a
phs
.
L
a
r
ge
-
s
c
a
le
n
e
twor
ks
s
ti
ll
f
in
d
th
e
s
e
ti
m
e
c
om
pl
e
xi
ty
to
be
pr
ohi
bi
ti
ve
,
c
ons
e
que
nt
ly
a
r
e
li
a
bl
e
a
nd
e
f
f
e
c
ti
ve
be
twe
e
nn
e
s
s
c
e
nt
r
a
li
ty
a
ppr
oxi
m
a
ti
on a
lg
or
it
hm
is
ne
c
e
s
s
a
r
y. L
e
t
G
=(
V
,E
)
be
a
gr
a
ph.
G
c
a
n be
e
it
he
r
di
r
e
c
te
d or
undir
e
c
te
d, a
nd t
he
e
dge
w
e
ig
ht
s
m
us
t
be
non
-
ne
ga
ti
ve
.
n
=|
V
|,
m
=|
E
|
a
nd
th
e
num
be
r
of
s
hor
te
s
t
pa
th
s
f
r
om
node
s
to
node
t
is
r
e
pr
e
s
e
nt
e
d
by
σ
st
,
w
hi
le
th
e
num
be
r
of
s
hor
te
s
t
pa
th
s
th
a
t
pa
s
s
vi
a
node
v
is
r
e
pr
e
s
e
nt
e
d
by
σ
st
(
v
)
.
T
he
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
or
B
C
va
lu
e
of
a
node
v
∈
V
in
a
gr
a
ph
G
=(
V
,
E
)
a
s
s
how
n i
n (
1)
:
(
)
=
∑
(
)
≠
≠
,
∈
(1
)
B
a
s
e
d on the
B
r
a
nde
’
s
a
lg
or
it
hm
’
s
pa
ir
de
pe
nde
nc
y, w
e
c
a
n de
r
iv
e
a
s
s
how
n i
n (
2)
:
(
)
=
(
)
∗
(
)
=
∑
(
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r
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a
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num
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t
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t
a
node
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a
s
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h
ot
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r
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s
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ount
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th
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ic
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ig
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k
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ig
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c
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ph
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h
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l
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n
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ve
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s
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it
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n
a
ve
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ge
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it
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r
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t
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nks
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two
node
s
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it
h
th
e
hi
ghe
s
t
de
gr
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th
e
ta
bl
e
,
node
s
1
a
nd
3
F
(
23)
a
nd
F
(
28)
,
ha
ve
10
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r
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c
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k, i
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h 10
ot
he
r
node
s
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n
c
ont
r
a
s
t,
N
ode
s
17
(
F
(
24)
)
a
nd
a
f
e
w
ot
he
r
s
ha
ve
onl
y
1
de
gr
e
e
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in
d
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c
a
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m
in
im
a
l
in
te
r
a
c
ti
on.
W
e
ig
ht
e
d
d
e
g
r
e
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
E
x
pl
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ac
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tw
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s
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1759
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nha
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s
th
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by
in
c
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por
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r
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te
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it
y.
F
or
in
s
ta
nc
e
,
node
10 (
F
(
34
)
)
ha
s
t
he
l
a
r
ge
s
t
w
e
ig
ht
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ndi
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vi
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r
a
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of
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1
(
F
(
23)
)
ha
s
a
w
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ig
ht
e
d de
gr
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of
3.49, indi
c
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ha
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ti
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2.
T
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a
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s
um
m
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l
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s
ul
ts
N
ode
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ID
L
a
be
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D
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B
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C
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C
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f
i
c
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e
nt
T
r
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a
ngl
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s
1
F
(
23)
10
3.49
3.61
1
0.76
34
2
F
(
30)
6
1.51
0.00
1
1.00
15
3
F
(
28)
10
3.12
6.46
1
0.69
31
4
F
(
53)
3
1.13
0.00
2
1.00
3
5
M
(
21)
8
2.69
0.42
1
0.93
26
6
M
(
36)
5
2.08
0.00
3
1.00
10
7
F
(
43)
3
1.13
0.00
2
1.00
3
8
F
(
38)
6
1.56
1.00
3
0.80
12
9
F
(
38)
3
1.45
0.00
2
1.00
3
10
F
(
34)
9
3.65
1.10
1
0.86
31
11
F
(
29)
5
1.31
0.76
1
0.80
8
12
M
(
30)
2
0.83
0.00
4
1.00
1
13
F
(
36)
5
2.03
0.00
3
1.00
10
14
F
(
37)
9
3.25
1.10
1
0.86
31
15
M
(
18)
4
1.63
1.67
2
0.83
5
16
F
(
15)
8
1.91
3.28
1
0.71
20
17
F
(
24)
1
0.15
0.00
2
0.00
0
18
F
(
37)
1
0.16
0.00
5
0.00
0
19
M
(
21)
4
1.54
7.00
2
0.50
3
20
M
(
15)
6
1.23
16.00
2
0.40
6
21
F
(
32)
9
3.24
1.75
1
0.81
29
22
F
(
37)
1
0.16
0.00
5
0.00
0
23
F
(
21)
6
2.05
1.00
3
0.80
12
24
F
(
25)
3
0.91
0.00
3
1.00
3
26
F
(
37)
2
0.90
0.00
4
1.00
1
27
F
(
29)
5
1.84
0.00
3
1.00
10
28
M
(
30)
2
0.61
0.00
4
1.00
1
29
M
(
22)
4
1.47
1.67
2
0.83
5
30
M
(
31)
8
2.62
0.42
1
0.93
26
31
F
(
15)
4
1.56
1.67
2
0.83
5
33
F
(
19)
3
0.82
0.00
1
1.00
3
34
F
(
23)
1
0.71
0.00
6
0.00
0
35
F
(
24)
1
0.71
0.00
6
0.00
0
36
F
(
25)
9
3.65
1.10
1
0.86
31
37
F
(
30)
6
2.06
1.00
3
0.80
12
T
ot
a
l
172
59.192
51.000
26.005
390
A
ve
r
a
ge
4.914
1.691
1.457
0.743
11.143
F
ig
ur
e
5. T
he
a
c
c
ut
a
ne
gr
a
ph di
s
ta
nc
e
r
e
por
t
(
be
twe
e
nn
e
s
s
c
e
nt
r
a
li
ty
di
s
tr
ib
ut
io
n)
:
pa
r
a
m
e
te
r
s
:
ne
twor
k
in
te
r
pr
e
ta
ti
on:
undir
e
c
te
d, r
e
s
ul
ts
:
di
a
m
e
te
r
:
4, r
a
di
us
:
1, a
ve
r
a
g
e
pa
th
l
e
ngt
h:
1.3984375
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1752
-
1762
1760
T
he
ne
twor
k
ha
s
a
to
ta
l
de
gr
e
e
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172
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to
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ht
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hi
s
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on s
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ll
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t
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s
. W
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ve
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y node
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n
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k i
n
te
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oughly f
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ve
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r
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d t
o by
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om
pone
nt
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s
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onne
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ti
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ty
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s
ts
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n node
s
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it
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n t
he
s
a
m
e
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om
pone
nt
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s
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onne
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ti
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ur
s
be
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e
e
n
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s
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te
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s
e
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a
r
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te
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om
pone
nt
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.
I
t
is
e
vi
de
nt
th
a
t
th
e
ne
twor
k
is
not
e
nt
ir
e
ly
c
onne
c
te
d
be
c
a
us
e
it
c
ons
is
t
s
of
m
ul
ti
pl
e
di
s
c
onne
c
te
d
c
om
pone
nt
s
(
num
be
r
e
d
f
r
om
1
th
r
ough
6)
.
A
lt
hough
th
e
r
e
a
r
e
is
ol
a
te
d
node
s
(
li
ke
node
17
w
it
h
c
om
pone
nt
num
be
r
2)
,
th
e
r
e
a
r
e
a
ls
o
la
r
ge
c
om
pone
nt
s
l
ik
e
2 a
nd 3. F
or
i
ns
ta
nc
e
, node
1 a
nd a
ddi
ti
o
na
l
node
s
l
ik
e
node
2 a
r
e
pa
r
t
of
c
om
pone
nt
1,
in
di
c
a
ti
ng
th
a
t
to
ge
th
e
r
th
e
y
c
on
s
ti
tu
te
a
c
ohe
r
e
nt
s
ubne
twor
k.
N
ode
12
is
a
m
e
m
be
r
of
c
om
pone
nt
4,
w
hi
c
h
m
e
a
ns
it
is
a
s
e
pa
r
a
te
is
ol
a
te
d
c
lu
s
te
r
.
T
r
ia
ngl
e
s
,
w
hi
c
h
a
r
e
c
o
m
pos
e
d
of
th
r
e
e
c
onne
c
te
d
node
s
,
r
e
pr
e
s
e
nt
th
e
num
be
r
of
tr
ia
ngl
e
r
e
la
ti
ons
hi
ps
th
a
t
a
node
is
a
m
e
m
be
r
of
.
S
tr
ong
c
om
m
uni
ty
s
tr
uc
tu
r
e
is
in
di
c
a
te
d
by
hi
gh
tr
ia
ngl
e
num
be
r
s
.
N
ode
1
is
in
vol
ve
d
in
34
tr
ia
ngl
e
s
,
in
di
c
a
ti
ng
a
hi
gh
num
be
r
of
th
r
e
e
-
w
a
y
in
te
r
a
c
ti
ons
.
N
ode
s
17
a
nd
a
f
e
w
ot
he
r
node
s
,
on
th
e
ot
he
r
h
a
nd,
do
not
f
or
m
a
ny
tr
ia
ngl
e
s
,
hi
ghl
ig
ht
in
g
th
e
ir
i
s
ol
a
ti
on
or
la
c
k
of
c
om
m
uni
ty
in
te
r
a
c
ti
on.
T
he
r
e
a
r
e
390
tr
ia
ngl
e
s
in
to
ta
l,
a
nd
e
a
c
h
node
ha
s
a
n
a
ve
r
a
ge
of
11.143
tr
ia
ngl
e
s
,
in
di
c
a
ti
ng
th
a
t
node
s
ty
pi
c
a
ll
y
be
lo
ng
to
s
m
a
ll
c
onne
c
te
d
gr
oupi
ngs
th
a
t
r
e
pr
e
s
e
nt
c
om
m
uni
ty
-
li
ke
i
nt
e
r
a
c
ti
on.
B
e
twe
e
nne
s
s
c
e
nt
r
a
li
ty
qua
nt
if
ie
s
a
node
’
s
im
por
ta
nc
e
f
or
e
s
t
a
bl
is
hi
ng
in
te
r
a
c
ti
ons
by
c
a
lc
ul
a
ti
ng
how
f
a
r
it
is
a
lo
ng
th
e
s
hor
te
s
t
pa
th
s
c
onne
c
ti
ng
ot
he
r
node
s
.
A
hi
gh
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
va
lu
e
in
di
c
a
te
s
th
a
t
th
e
node
s
e
r
ve
s
a
s
a
ne
twor
k
br
id
ge
w
it
hi
n
th
e
ne
twor
k.
T
he
a
ve
r
a
ge
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
is
1.457,
m
e
a
ni
ng
th
a
t
node
s
ha
ve
a
m
ode
r
a
te
im
pa
c
t
on
e
s
ta
bl
is
hi
ng
c
onne
c
ti
ons
be
twe
e
n
ot
he
r
node
s
.
T
he
to
ta
l
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
is
51.
A
s
a
n
il
lu
s
tr
a
ti
on,
nod
e
3
(
F
(
28)
)
ha
s
a
c
on
s
id
e
r
a
bl
e
b
e
twe
e
nne
s
s
c
e
nt
r
a
li
ty
of
6.46,
r
e
f
le
c
ti
ng
it
s
s
ig
ni
f
ic
a
nc
e
in
c
onne
c
ti
ng
to
ge
th
e
r
th
e
ne
t
w
or
k’
s
e
ls
e
w
he
r
e
s
e
pa
r
a
te
d
s
e
c
ti
ons
.
W
it
h
a
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
of
16.00,
node
20
(
M
(
15)
)
ha
s
th
e
hi
ghe
s
t
be
twe
e
nne
s
s
a
nd
i
s
th
e
r
e
f
or
e
ve
r
y
im
por
ta
nt
to
th
e
in
f
o
r
m
a
ti
on
f
lo
w
a
c
r
os
s
th
e
ne
twor
k’
s
c
om
m
uni
c
a
ti
on
s
tr
uc
tu
r
e
.
O
n
th
e
o
th
e
r
ha
nd,
a
la
r
ge
num
be
r
of
node
s
ha
ve
a
be
twe
e
nne
s
s
c
e
nt
r
a
li
ty
of
0,
in
di
c
a
ti
n
g
th
a
t
th
e
y
a
r
e
not
c
e
nt
r
a
l
or
a
c
t
a
s
out
s
id
e
r
s
.
T
he
c
lu
s
t
e
r
in
g
c
oe
f
f
ic
ie
nt
in
di
c
a
te
s
th
e
de
gr
e
e
of
lo
c
a
l
c
ohe
s
i
ve
ne
s
s
or
c
li
qui
s
hne
s
s
(
pr
oduc
in
g
a
c
om
pl
e
te
c
li
que
)
by
c
a
lc
ul
a
ti
ng
th
e
de
gr
e
e
to
w
hi
c
h
a
nod
e
’
s
ne
ig
hbo
r
s
a
r
e
c
onn
e
c
te
d
to
one
a
not
h
e
r
.
A
c
oh
e
s
iv
e
c
om
m
uni
ty
is
in
di
c
a
te
d
by
a
hi
gh
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
va
lu
e
.
A
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
of
0.00
(
a
s
s
e
e
n
in
node
17)
in
di
c
a
te
s
th
a
t
th
e
node
’
s
n
e
ig
hbor
s
do
not
f
or
m
a
ny
t
r
ia
ngl
e
s
,
w
hi
le
a
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
of
1.00
(
a
s
in
node
2)
im
pl
ie
s
th
a
t
a
ll
lo
c
a
l
ne
ig
hbor
s
a
r
e
f
ul
ly
c
onne
c
te
d
a
nd
c
ont
r
ib
ut
in
g
to
a
t
ig
ht
ly
bound
(
hi
ghl
y
c
onne
c
te
d)
c
lu
s
te
r
.
T
he
n
e
twor
k’
s
a
ve
r
a
ge
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
of
0.743
in
di
c
a
te
s
th
a
t
node
s
a
r
e
f
a
ir
ly
c
lu
s
te
r
e
d w
it
h m
a
ny t
ig
ht
ly
c
onne
c
te
d gr
oups
a
nd
a
hi
gh t
e
nde
n
c
y f
or
l
oc
a
l
c
lu
s
te
r
in
g.
4.
C
O
N
C
L
U
S
I
O
N
T
hi
s
pa
pe
r
pr
e
s
e
nt
s
a
s
tu
dy
on
th
e
c
onc
e
pt
ua
li
z
a
ti
on
of
P
P
I
s
g
r
a
ph
a
s
a
ne
twor
k
a
nd
a
na
ly
z
in
g
th
is
ne
twor
k
by
m
e
a
ns
of
s
e
le
c
te
d
ne
twor
k
a
n
a
ly
s
is
a
ppr
oa
c
he
s
th
r
ough
th
e
e
xpl
or
a
ti
on
of
th
e
hi
dde
n
pa
tt
e
r
ns
of
r
e
a
c
ti
vi
ty
a
nd
c
onne
c
ti
vi
ty
a
m
ong
in
te
r
c
ha
ngi
ng
node
s
.
T
he
P
P
I
s
ne
twor
k
of
A
c
c
ut
a
ne
dr
ug
w
a
s
s
e
le
c
te
d.
T
he
pr
oc
e
s
s
of
th
e
a
na
ly
s
is
of
th
is
P
P
I
s
ne
twor
k
w
a
s
e
xpl
a
in
e
d
in
de
ta
il
a
nd
th
us
th
e
obj
e
c
ti
ve
f
or
th
is
s
tu
dy
w
a
s
a
ddr
e
s
s
e
d.
I
t
c
a
n
be
ob
s
e
r
ve
d
f
r
om
th
e
f
or
e
goi
ng
th
a
t:
i)
r
e
s
e
a
r
c
h
que
s
ti
on
(
Q
1:
A
r
e
p
a
ti
e
nt
s
c
ons
is
t
e
nt
ly
r
e
s
ponding
to
pa
ti
e
nt
s
be
lo
ngi
ng
to
a
s
im
il
a
r
ge
nde
r
a
nd/
or
a
ge
pr
of
il
e
?
)
w
a
s
a
n
s
w
e
r
e
d
ne
ga
ti
ve
ly
be
c
a
u
s
e
th
e
ne
twor
k
e
xhi
bi
te
d
hi
gh
de
gr
e
e
s
of
he
te
r
oge
ne
it
y
w
it
h
r
e
s
p
e
c
t
to
ge
nde
r
a
nd/
or
a
ge
pr
of
il
e
w
it
h
di
f
f
e
r
e
nt
va
lu
e
s
.
T
hi
s
e
m
pha
s
iz
e
d
a
di
s
a
gr
e
e
m
e
nt
b
e
twe
e
n
ge
nde
r
a
nd
a
ge
pr
of
il
e
.
P
a
ti
e
nt
s
te
nd
th
e
r
e
f
or
e
to
in
te
r
a
c
t
w
it
h
pa
ti
e
nt
s
w
it
h
di
f
f
e
r
e
nt
ge
nde
r
a
nd/
o
r
a
ge
pr
o
f
il
e
.
ii
)
r
e
s
e
a
r
c
h
que
s
ti
on
(
Q
2:
A
r
e
c
om
m
uni
ti
e
s
f
ound
i
n
th
e
(
P
P
I
s
)
ne
two
r
k
a
bl
e
to
id
e
nt
i
f
y,
a
t
le
a
s
t
r
oughly,
a
pa
ti
e
n
t
’
s
a
ge
or
ge
nde
r
?
)
w
a
s
a
ns
w
e
r
e
d
ne
ga
ti
ve
ly
be
c
a
us
e
none
of
th
e
c
on
s
id
e
r
e
d
c
om
m
uni
ty
de
te
c
ti
on
a
ppr
oa
c
h
e
s
w
a
s
a
bl
e
to
d
e
te
c
t
c
om
m
uni
ti
e
s
,
w
it
hi
n
th
e
ne
twor
k, of
m
e
m
be
r
s
ha
vi
ng t
he
s
a
m
e
ge
nde
r
or
t
he
s
a
m
e
a
ge
p
r
of
il
e
. S
om
e
c
om
m
uni
ti
e
s
(
e
.g., c
om
pone
nt
1)
c
ont
a
in
e
d
node
s
(
pa
ti
e
nt
s
)
f
r
om
di
f
f
e
r
e
nt
a
ge
a
nd
ge
nde
r
p
r
of
il
e
s
.
O
th
e
r
c
om
pone
nt
s
(
li
ke
4
a
nd
5)
a
r
e
m
uc
h
s
m
a
ll
e
r
a
nd c
oul
d
r
e
pr
e
s
e
nt
m
or
e
hom
oge
nou
s
gr
oups
c
ons
id
e
r
in
g
a
ge
or
g
e
nde
r
. T
hus
it
w
a
s
c
on
c
lu
de
d
th
a
t,
th
e
c
om
m
uni
ty
s
tr
uc
tu
r
e
(
id
e
nt
if
ie
d
by
c
om
pone
nt
num
be
r
s
)
c
ont
a
in
node
s
w
it
h
hi
gh
c
lu
s
te
r
in
g
c
oe
f
f
ic
ie
nt
s
s
e
e
m
e
d
not
to
be
hi
ghl
y
c
or
r
e
la
te
d
w
it
h
a
ge
or
ge
nde
r
.
T
he
s
e
c
om
m
uni
ti
e
s
m
a
y
be
f
or
m
e
d
ba
s
e
d
on
ot
he
r
f
a
c
to
r
s
li
ke
s
im
il
a
r
it
y
be
twe
e
n
pa
ti
e
nt
s
’
r
e
vi
e
w
s
le
xi
c
a
l
c
o
nt
e
nt
s
or
s
e
nt
im
e
nt
s
,
r
a
th
e
r
th
a
n
ge
nde
r
or
de
m
ogr
a
phi
c
c
ha
r
a
c
te
r
is
ti
c
s
,
i.
e
.,
a
ge
or
ge
nde
r
w
a
s
not
a
do
m
in
a
nt
f
a
c
to
r
in
de
te
r
m
in
in
g
m
e
m
be
r
s
hi
p
in
th
e
s
e
c
om
m
uni
ti
e
s
.
M
a
ny
pr
om
is
in
g
f
ut
ur
e
r
e
s
e
a
r
c
h
di
r
e
c
ti
ons
pr
e
s
e
nt
th
e
m
s
e
lv
e
s
,
s
o
a
s
to
e
xt
e
nd
th
e
f
unc
ti
ona
li
ty
a
nd
e
nha
nc
e
th
e
ope
r
a
ti
on
of
a
na
ly
z
in
g
la
r
ge
c
ol
le
c
ti
ons
of
P
P
I
s
ne
twor
ks
di
r
e
c
tl
y
us
in
g
gr
a
ph
m
in
in
g a
ppr
oa
c
he
s
r
a
th
e
r
t
ha
n us
in
g ba
s
ic
t
a
bul
a
r
da
ta
a
n
a
ly
s
is
te
c
hni
que
s
.
F
U
N
D
I
N
G
I
N
F
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,
upon r
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que
s
t.
R
E
F
E
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C
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S
[
1]
H
. B
a
of
a
ng, H
. W
a
ng, L
.
W
a
ng, a
nd
W
. Y
ua
n.
“
A
dv
e
r
s
e
dr
ug r
e
a
c
t
i
on pr
e
di
c
t
i
ons
us
i
ng
s
t
a
c
ki
ng de
e
p
h
e
t
e
r
oge
ne
ous
i
nf
or
m
a
t
i
on
ne
t
w
or
k e
m
be
ddi
ng a
ppr
oa
c
h,
”
M
ol
e
c
ul
e
s
, vol
.
23, no. 12, 2018, doi
:
10.3390/
m
ol
e
c
ul
e
s
23123193
.
[
2]
X
.
Z
ha
o,
L
.
C
he
n,
Z
.
H
.
G
uo,
a
nd
T
.
L
i
u.
“
P
r
e
di
c
t
i
ng
dr
ug
s
i
de
e
f
f
e
c
t
s
w
i
t
h
c
om
pa
c
t
i
nt
e
gr
a
t
i
on
o
f
he
t
e
r
oge
ne
ous
ne
t
w
or
ks
,
”
C
ur
r
e
nt
B
i
oi
nf
or
m
at
i
c
s
, vol
.
14,
no. 8
, pp.
709
-
720
, 2019,
doi
:
10.2174/
1574893614666190220114644
.
[
3]
H
.
B
a
of
a
ng,
H
.
W
a
ng,
a
nd
Z
.
Y
u.
“
D
r
ug
s
i
de
-
e
f
f
e
c
t
pr
e
di
c
t
i
on
vi
a
r
a
ndom
w
a
l
k
on
t
he
s
i
gne
d
he
t
e
r
oge
ne
ous
dr
ug
ne
t
w
or
k
,
”
M
ol
e
c
ul
e
s
, vol
.
24, no. 20
,
2019
,
doi
:
10.3390/
m
ol
e
c
ul
e
s
24203668
.
[
4]
C.
C
.
Y
a
ng
a
nd
M
.
Z
ha
o.
“
M
i
ni
ng
he
t
e
r
oge
ne
ou
s
ne
t
w
or
k
f
or
dr
ug
r
e
po
s
i
t
i
oni
ng
us
i
ng
phe
not
ypi
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i
nf
or
m
a
t
i
on
e
xt
r
a
c
t
e
d
f
r
om
s
oc
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a
l
m
e
di
a
a
nd
pha
r
m
a
c
e
ut
i
c
a
l
d
a
t
a
ba
s
e
s
,
”
A
r
t
i
f
i
c
i
al
I
nt
e
l
l
i
ge
nc
e
i
n
M
e
di
c
i
ne
,
vol
.
96
,
pp.
80
-
92
,
2019,
doi
:
10.1016/
j
.a
r
t
m
e
d.2019.03.003
.
[
5]
L
.
P
e
ng,
C
.
Y
a
ng,
Y
.
C
he
n,
a
nd
W
.
L
i
u.
“
P
r
e
di
c
t
i
ng
C
i
r
c
R
N
A
-
D
i
s
e
a
s
e
a
s
s
oc
i
a
t
i
ons
vi
a
f
e
a
t
ur
e
c
onvol
ut
i
on
l
e
a
r
ni
ng
w
i
t
h
he
t
e
r
oge
ne
ous
gr
a
ph a
t
t
e
nt
i
on ne
t
w
or
k
,
”
I
E
E
E
J
our
nal
of
B
H
I
27, no. 6
, pp.
30
72
-
3082
, 2023,
doi
:
10.1109/
J
B
H
I
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[
6]
P
.
C
ha
nda
k,
K
.
H
ua
ng
,
a
nd
M
.
Z
i
t
ni
k.
“
B
ui
l
di
ng
a
know
l
e
dge
gr
a
ph
t
o
e
na
bl
e
pr
e
c
i
s
i
on
m
e
di
c
i
ne
,
”
Sc
i
e
nt
i
f
i
c
D
at
a
10,
no.
1
,
2023,
doi
:
10.1038/
s
41597
-
023
-
01960
-
3
.
[
7]
G
.
K
he
ka
r
e
,
S
.
G
huga
r
e
,
R
.
K
ha
t
r
i
,
G
.
M
a
j
um
de
r
,
a
nd
U
.
K
he
ka
r
e
,
“
B
l
oc
kc
ha
i
n
pow
e
r
e
d
i
nt
e
gr
a
t
e
d
he
a
l
t
h
pr
of
i
l
e
a
nd
r
e
c
or
d
m
a
na
ge
m
e
nt
s
y
s
t
e
m
f
or
s
e
a
m
l
e
s
s
c
ons
ul
t
a
t
i
on l
e
ve
r
a
gi
ng
uni
que
i
de
nt
i
f
i
e
r
s
,
”
2
024 Se
c
ond I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on E
m
e
r
gi
n
g
T
r
e
nds
i
n
I
nf
or
m
at
i
on
T
e
c
hnol
ogy
and
E
ngi
ne
e
r
i
ng
(
I
C
E
T
I
T
E
)
,
V
e
l
l
or
e
,
I
ndi
a
,
pp.
1
-
9
,
2024,
doi
:
10.1109/
i
c
-
E
T
I
T
E
58242.2024.10493266
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[
8]
Z
.
S
a
l
a
h,
E
.
E
l
s
oud,
K
.
S
a
l
a
h,
W
.
T
.
A
l
-
S
i
t
,
M
.
M
a
a
ya
’
a
,
a
nd
A
.
A
l
K
ha
w
a
l
de
h
,
“
P
a
t
i
e
nt
-
pa
t
i
e
nt
i
nt
e
r
a
c
t
i
ons
vi
s
ua
l
i
z
a
t
i
on
f
or
dr
ug
s
i
de
e
f
f
e
c
t
s
i
n
pa
t
i
e
nt
s
’
r
e
vi
e
w
s
,
”
I
ndone
s
i
an
J
our
nal
of
E
l
e
c
t
r
i
c
al
E
ngi
ne
e
r
i
ng
and
C
om
put
e
r
Sc
i
e
n
c
e
,
v
ol
.
34,
n
o.
3,
pp.
2007
-
2020,
J
un
.
2024,
doi
:
10.11591/
i
j
e
e
c
s
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3.pp2007
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2020.
[
9]
A
.
B
e
r
m
i
ngha
m
,
M
.
C
onw
a
y,
L
.
M
c
I
ne
r
ne
y,
N
.
O
’
H
a
r
e
,
a
nd
A
.
F
.
S
m
e
a
t
on.
“
C
om
bi
ni
ng
s
oc
i
a
l
ne
t
w
or
k
a
na
l
ys
i
s
a
nd
s
e
nt
i
m
e
nt
a
na
l
ys
i
s
t
o
e
xpl
or
e
t
he
pot
e
nt
i
a
l
f
or
onl
i
ne
r
a
di
c
a
l
i
s
a
t
i
on,
”
I
n
P
r
oc
e
e
di
ngs
of
t
he
2009
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
A
dv
anc
e
s
i
n
Soc
i
al
N
e
t
w
or
k
A
nal
y
s
i
s
and
M
i
ni
ng,
A
SO
N
A
M
‘
09
,
p
p.
231
–
236,
W
a
s
hi
ngt
on,
D
C
,
U
S
A
,
2009
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doi
:
10.1109/
A
S
O
N
A
M
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[
10]
P.
A
.
G
l
oor
,
J
.
K
r
a
us
s
,
S
.
N
a
nn,
K
.
F
i
s
c
hba
c
h
,
a
nd
D
.
S
c
hode
r
,
“
W
e
b
s
c
i
e
n
c
e
2.0:
I
de
nt
i
f
yi
ng
t
r
e
nds
t
hr
ough
s
e
m
a
nt
i
c
s
oc
i
a
l
ne
t
w
or
k a
na
l
ys
i
s
,
”
2009 i
nt
e
r
nat
i
onal
c
onf
e
r
e
nc
e
on C
SE
, v
ol
. 4
,
I
E
E
E
, 2009
,
doi
:
10.1109/
C
S
E
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[
11]
J
.
R
a
be
l
o,
R
.
B.
C
.
P
r
ude
nc
i
o
,
a
nd
F
.
B
a
r
r
os
,
“
C
ol
l
e
c
t
i
ve
c
l
a
s
s
i
f
i
c
a
t
i
on
f
or
s
e
nt
i
m
e
nt
a
na
l
ys
i
s
i
n
s
oc
i
a
l
ne
t
w
or
ks
,
”
I
n
P
r
oc
e
e
di
ng
s
of
t
he
2012
I
E
E
E
24t
h
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
T
ool
s
w
i
t
h
A
r
t
i
f
i
c
i
al
I
nt
e
l
l
i
ge
nc
e
,
W
a
s
hi
ngt
on,
U
S
A
,
2012,
p
p.
958
-
963,
doi
:
10.1109/
I
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T
A
I
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[
12]
C
.
W
a
ng,
Z
.
X
i
a
o,
Y
.
L
i
u,
Y
.
X
u,
A
.
Z
hou
,
a
nd
K
.
Z
ha
ng.
“
S
e
nt
i
vi
e
w
:
s
e
nt
i
m
e
nt
a
na
l
ys
i
s
a
nd
vi
s
ua
l
i
z
a
t
i
on
f
or
i
nt
e
r
ne
t
popul
a
r
t
opi
c
s
,
”
I
E
E
E
t
r
ans
ac
t
i
ons
on hum
an
-
m
ac
hi
ne
s
y
s
t
e
m
s
,
vol
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620
-
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:
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[
13]
M
.
S
ha
m
s
,
M
.
S
a
f
f
a
r
,
A
.
S
ha
ke
r
y
,
a
nd
H
.
F
a
i
l
i
.
“
A
ppl
yi
ng
s
e
nt
i
m
e
nt
a
nd
s
oc
i
a
l
ne
t
w
or
k
a
na
l
ys
i
s
i
n
u
s
e
r
m
ode
l
l
i
ng
,
”
I
n
P
r
oc
e
e
di
ngs
of
t
h
e
13t
h
I
nt
e
r
nat
i
onal
C
onf
e
r
e
n
c
e
on
C
om
put
at
i
onal
L
i
ngui
s
t
i
c
s
and
I
nt
e
l
l
i
ge
nt
T
e
x
t
P
r
oc
e
s
s
i
ng,
B
e
r
l
i
n,
H
e
i
de
l
be
r
g,
p
p.
526
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539, 2012
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:
10.1007/
978
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3
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642
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28604
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9 43.
[
14]
W
.
D
e
i
t
r
i
c
k
a
nd
W
.
H
u.
“
M
ut
ua
l
l
y
e
nha
nc
i
ng
c
om
m
uni
t
y
de
t
e
c
t
i
on
a
nd
s
e
nt
i
m
e
nt
a
na
l
ys
i
s
on
t
w
i
t
t
e
r
ne
t
w
or
ks
,
”
J
our
nal
of
D
at
a
A
nal
y
s
i
s
and I
nf
or
m
at
i
on P
r
oc
e
s
s
i
ng
,
vol
.
1
, no.
3
, pp.
19
-
29, 2013
, doi
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j
da
i
p.2013.13004.
[
15]
H
.
D
e
ng,
J
.
H
a
n,
H
.
J
i
,
H
.
L
i
,
Y
.
L
u
,
a
nd
H
.
W
a
ng.
“
E
xpl
or
i
ng
a
nd
i
nf
e
r
r
i
ng
us
e
r
-
us
e
r
ps
e
udo
-
f
r
i
e
nds
hi
p
f
or
s
e
nt
i
m
e
nt
a
na
l
y
s
i
s
w
i
t
h
he
t
e
r
oge
ne
ous
ne
t
w
or
ks
,
”
St
at
i
s
t
i
c
al
A
nal
y
s
i
s
and
D
at
a
M
i
ni
ng:
T
he
A
S
A
D
at
a
Sc
i
e
nc
e
J
our
nal
,
vol
.
7
,
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4,
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