Acta Physica Sinica,
Год журнала:
2025,
Номер
74(12), С. 0 - 0
Опубликована: Янв. 1, 2025
Identifying
key
nodes
in
complex
networks
or
evaluating
the
relative
node
importance
with
respect
to
others
using
quantitative
methods
is
a
fundamental
issue
network
science.
To
address
limitations
of
existing
approaches—namely
subjectivity
assigning
weights
indicators
and
insufficient
integration
global
local
structural
information—this
paper
proposes
an
entropy-weighted
multi-channel
convolutional
neural
framework
(EMCNN).
First,
parameter-free
entropy-based
weight
allocation
model
constructed
dynamically
assign
multiple
by
computing
their
entropy
values,
thereby
mitigating
inherent
traditional
parameter-setting
enhancing
objectivity
indicator
fusion.
Second,
features
are
decoupled
reconstructed
into
separate
channels
form
feature
maps,
which
significantly
enhance
representational
capacity
structure.
Third,
leveraging
extraction
capabilities
power
attention
mechanisms,
extracts
deep
representations
from
while
emphasizing
information
through
attention-based
weighting,
thus
enabling
more
accurate
identification
characterization
importance.
validate
effectiveness
proposed
method,
extensive
experiments
conducted
on
nine
real-world
SIR
spreading
model,
assessing
performance
terms
correlation,
accuracy,
robustness.
The
Kendall
correlation
coefficient
employed
as
primary
evaluation
metric
measure
consistency
between
predicted
actual
influence.
Additionally,
performed
three
representative
synthetic
further
test
model’s
generalizability.
Experimental
results
demonstrate
that
EMCNN
consistently
effectively
evaluates
influence
under
varying
transmission
rates,
outperforms
mainstream
algorithms
both
accuracy.
These
findings
highlight
method’s
strong
generalization
ability
broad
applicability
tasks
within
networks.
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Июль 14, 2023
Abstract
Centrality
analysis
is
a
crucial
tool
for
understanding
the
role
of
nodes
in
network,
but
it
unclear
how
different
centrality
measures
provide
much
unique
information.
To
improve
identification
influential
we
propose
new
method
called
Hybrid-GSM
(H-GSM)
that
combines
K-shell
decomposition
approach
and
Degree
Centrality.
H-GSM
characterizes
impact
more
precisely
than
Global
Structure
Model
(GSM),
which
cannot
distinguish
importance
each
node.
We
evaluate
performance
using
SIR
model
to
simulate
propagation
process
six
real-world
networks.
Our
outperforms
other
approaches
regarding
computational
complexity,
node
discrimination,
accuracy.
findings
demonstrate
proposed
as
an
effective
identifying
complex
Mathematics,
Год журнала:
2024,
Номер
12(18), С. 2898 - 2898
Опубликована: Сен. 17, 2024
This
paper
presents
a
comprehensive
study
of
the
linguistic
Z-graph,
which
is
novel
framework
designed
to
analyze
structures
within
social
networks.
By
integrating
concepts
from
graph
theory
and
linguistics,
Z-graph
provides
detailed
understanding
language
dynamics
in
online
communities.
highlights
practical
applications
Z-graphs
identifying
central
nodes
networks,
are
crucial
for
businesses
market
capture
information
dissemination.
Traditional
methods
rely
on
direct
connections,
but
network
connections
often
exhibit
uncertainty.
focuses
using
fuzzy
theory,
particularly
Z-graphs,
address
this
uncertainty,
offering
more
insights
compared
graphs.
Our
introduces
new
centrality
measure
enhancing
our
structures.
Journal of King Saud University - Computer and Information Sciences,
Год журнала:
2023,
Номер
35(10), С. 101798 - 101798
Опубликована: Окт. 16, 2023
The
influence
of
the
node
refers
to
ability
disseminate
information.
faster
and
wider
spreads,
greater
its
influence.
There
are
many
classical
topological
metrics
that
can
be
used
evaluate
influencing
nodes.
Degree
centrality,
betweenness
closeness
centrality
local
among
most
common
for
identifying
influential
nodes
in
complex
networks.
is
very
simple
but
not
effective.
Global
such
as
better
identify
nodes,
they
compatible
on
large-scale
networks
due
their
high
complexity.
In
order
design
a
ranking
method
this
paper
new
semi-local
metric
proposed
based
relative
change
average
shortest
path
entire
network.
Meanwhile,
our
provides
quantitative
global
importance
model
measure
overall
each
node.
To
performance
metric,
we
use
Susceptible-Infected-Recovered
(SIR)
epidemic
model.
Experimental
results
several
real-world
show
has
competitive
with
existing
equivalent
efficiency
dealing
effectiveness
been
proven
numerical
examples
Kendall's
coefficient.
PLoS ONE,
Год журнала:
2024,
Номер
19(7), С. e0306561 - e0306561
Опубликована: Июль 18, 2024
Theoretical
and
empirical
studies
on
diffusion
models
have
revealed
their
versatile
applicability
across
different
fields,
spanning
from
sociology
finance
to
biology
ecology.
The
presence
of
a
community
structure
within
real-world
networks
has
substantial
impact
how
processes
unfold.
Key
nodes
located
both
between
these
communities
play
crucial
role
in
initiating
diffusion,
community-aware
centrality
measures
effectively
identify
nodes.
While
numerous
been
proposed
literature,
very
few
investigate
the
relationship
diffusive
ability
key
selected
by
measures,
distinct
dynamical
conditions
various
models,
diverse
network
topologies.
By
conducting
comparative
evaluation
four
utilizing
synthetic
networks,
along
with
employing
two
detection
techniques,
our
study
aims
gain
deeper
insights
into
effectiveness
measures.
Results
suggest
that
power
is
affected
three
main
factors:
strength
network’s
structure,
internal
dynamics
each
model,
budget
availability.
Specifically,
category
simple
contagion
such
as
SI,
SIR,
IC,
we
observe
similar
patterns
when
remain
constant.
In
contrast,
LT
which
falls
under
complex
dynamics,
exhibits
divergent
behavior
same
conditions.
