Mining
influential
nodes
in
complex
networks
has
been
a
topic
of
immense
interest
recent
years.
Various
algorithms
have
proposed
to
tackle
this
problem.
However,
they
are
subject
single
dimensions
or
overlook
the
nodes'
connections.
This
paper
proposes
novel
centrality
measure
based
on
Tsallis
entropy
and
Laplacian
(TL).
TL
treats
influence
as
non-extensive
attribute
incorporates
interactions
subsystems
by
combining
centrality,
Structural
hole,
modified
entropy.
In
addition,
K-shell
iteration
factor
H-index
value
separately
considered
construct
global
local
spatial
information.
Finally,
aggregates
influences
both
node
itself
its
neighbors.
Experiments
conducted
nine
compared
six
other
methods
verify
effectiveness
our
method.
The
experimental
results
indicate
that
ranks
most
important
with
higher
Kendall's
$\tau$
coefficient
monotonicity.
well
performance
monotonicity
well.
terms
infection
ability,
top
identified
also
exhibit
superior
spreading
capability
play
an
role
maintaining
structure
networks,
establishing
method
for
identifying
nodes.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 11, 2025
Identifying
influential
nodes
in
real
networks
is
significant
studying
and
analyzing
the
structural
as
well
functional
aspects
of
networks.
VoteRank
a
simple
effective
algorithm
to
identify
high-spreading
nodes.
The
accuracy
monotonicity
are
poor
network
topology
fails
be
taken
into
account.Given
nodes'
attributes
neighborhood
structure,
this
paper
put
forward
an
based
on
Edge
Weighted
(EWV)
for
identifying
network.
proposed
draws
inspiration
from
human
voting
behavior
expresses
attractiveness
their
first-order
using
weights
connecting
edges.
Similarity
between
introduced
process,
further
enhancing
method.
Additionally,
EWV
addresses
problem
node
clustering
by
reducing
ability
second-order
most
validity
presented
verified
through
experiments
conducted
12
different
various
sizes
structures,
directly
comparing
it
with
7
competing
algorithms.Empirical
results
indicate
superiority
over
remaining
seven
algorithms
respect
differentiation
ability,
effectiveness,
ranked
list
accuracy.
Transactions in GIS,
Год журнала:
2025,
Номер
29(2)
Опубликована: Фев. 28, 2025
ABSTRACT
Identifying
key
influential
nodes
in
Earth
surface
data
association
networks
is
crucial
for
optimizing
the
use
of
scientific
data.
However,
challenges
such
as
network
size,
complexity,
and
dynamic
node
influence
make
this
task
difficult.
While
deep
learning
methods
have
improved
recognition
accuracy
reduced
computational
costs
complex
networks,
they
still
struggle
with
balancing
efficiency
accuracy.
To
address
this,
we
propose
DCKH‐CNN,
a
novel
Multimetric
Graph‐Based
Convolutional
Neural
Network
framework.
Based
on
LCNN
model,
it
integrates
global
local
features
by
calculating
metrics
degree
centrality,
K
‐shell,
H
‐index,
near‐centrality.
One‐hop
two‐hop
adjacency
matrices
are
used
to
represent
internode
relationships,
enhancing
feature
representation.
Trained
small‐scale
model
captures
unique
characteristics.
Experimental
results
using
SIR
demonstrate
that
DCKH‐CNN
surpasses
state‐of‐the‐art
algorithms
vast
majority
Surface
Data
Linked
(ESSDLN)
datasets
real‐world
accuracy,
while
demonstrating
moderate
time
consumption.
This
method
offers
more
efficient
approach
identifying
supporting
accurate
recommendations
intelligent
analysis
International Journal of Electrical Power & Energy Systems,
Год журнала:
2023,
Номер
154, С. 109431 - 109431
Опубликована: Авг. 21, 2023
The
identification
of
critical
nodes
is
important
for
safe
operation
and
accident
prevention
in
power
grids.
With
the
accelerated
development
renewable
energies,
uncertainty
energy
has
brought
greater
challenges
to
node
importance
assessment
system.
electrical
spreading
probability
method
proposed
this
paper
identify
grid
containing
energy.
First,
factors
are
established
through
density
functions,
Monte
Carlo
simulation
stochastic
DC
optimum
flow
adopted
effectively
deal
with
impact
uncertain
output
from
solar
wind
energies.
Then,
considering
system
topology
load
loss
after
cascading
failures
calculated
by
simulation,
a
calculate
being
infected.
Finally,
according
Susceptible–Infected
model,
depending
on
propagation
ability
complex
effectiveness
ESP
verified
examples
modified
IEEE39
IEEE118
Chinese Physics B,
Год журнала:
2024,
Номер
33(5), С. 058901 - 058901
Опубликована: Янв. 22, 2024
In
recent
years,
exploring
the
relationship
between
community
structure
and
node
centrality
in
complex
networks
has
gained
significant
attention
from
researchers,
given
its
fundamental
theoretical
significance
practical
implications.
To
address
impact
of
network
communities
on
target
nodes
effectively
identify
highly
influential
with
strong
propagation
capabilities,
this
paper
proposes
a
novel
spreaders
identification
algorithm
based
density
entropy
(DECS).
The
proposed
method
initially
integrates
detection
to
obtain
partition
results
networks.
It
then
comprehensively
considers
internal
external
entropies
degree
evaluate
influence.
Experimental
validation
is
conducted
eight
varying
sizes
through
susceptible–infected–recovered
(SIR)
experiments
static
attack
experiments.
experimental
demonstrate
that
outperforms
five
other
methods
under
same
comparative
conditions,
particularly
terms
information
spreading
capability,
thereby
enhancing
accurate
critical
