Chinese Physics B,
Journal Year:
2024,
Volume and Issue:
33(8), P. 088901 - 088901
Published: June 3, 2024
Abstract
The
virtuality
and
openness
of
online
social
platforms
make
networks
a
hotbed
for
the
rapid
propagation
various
rumors.
In
order
to
block
outbreak
rumor,
one
most
effective
containment
measures
is
spreading
positive
information
counterbalance
diffusion
rumor.
mechanism
rumors
suppression
strategies
are
significant
challenging
research
issues.
Firstly,
in
simulate
dissemination
multiple
types
information,
we
propose
competitive
linear
threshold
model
with
state
transition
(CLTST)
describe
process
rumor
anti-rumor
same
network.
Subsequently,
put
forward
community-based
blocking
(CRB)
algorithm
based
on
influence
maximization
theory
networks.
Its
crucial
step
identify
set
influential
seeds
that
propagate
other
nodes,
which
includes
community
detection,
selection
candidate
generation
seed
set.
Under
CLTST
model,
CRB
has
been
compared
six
state-of-the-art
algorithms
nine
verify
performance.
Experimental
results
show
proposed
can
better
reflect
propagation,
review
Moreover,
performance
weakening
ability,
select
more
accurately
achieve
spread,
sensitivity
analysis,
distribution
running
time.
Information Sciences,
Journal Year:
2023,
Volume and Issue:
644, P. 119284 - 119284
Published: June 15, 2023
With
the
expansion
of
number
users
in
online
social
networks,
diversity
and
community
characteristics
become
more
prominent.
Hypernetwork
theory
provides
a
path
for
characterizing
complex
relationships
networks.
This
paper
used
hypergraph's
hyperedges
to
represent
relationship
between
users,
created
an
hypernetwork
information
dissemination
model
(UHIR
model)
based
on
user
attributes
by
combining
with
SEIR
model.
Through
this
model,
article
simulated
analyzed
dynamic
process
laws
under
different
network
structures,
studied
influence
influence,
confidence,
interest
value,
timeliness
process.
The
simulation
results
show
that
can
accurately
describe
trend
real
network.
work
extends
new
research
direction
hypernetworks
contributes
in-depth
study
mechanisms.
Information Processing & Management,
Journal Year:
2024,
Volume and Issue:
61(3), P. 103683 - 103683
Published: Feb. 15, 2024
Influence
Maximization
(IM)
has
promising
applications
in
social
network
marketing
and
been
extensively
researched
over
the
past
years.
However,
previous
IM
studies
mainly
focus
on
ordinary
graphs
rather
than
hypergraphs,
where
edges
cannot
accurately
describe
group
interactions
or
relationships.
To
model
interactions,
we
investigate
problem
hypergraphs
under
Susceptible–Infected
spreading
with
Contact
Process
dynamics
(SICP)
this
paper.
In
paper,
proposed
a
probability
distribution-based
method,
called
Multi-hop
Estimation
(MIE),
which
can
estimate
rank
of
influence
expectation
nodes,
to
solve
hypergraphs.
Specifically,
compute
score
for
each
node
through
constrained
Depth
First
Search
(DFS)
model,
then
select
seed
according
score.
addition,
by
analysing
characteristics
diffusion
find
that
is
significantly
related
its
neighbourhood
structure.
Based
observation,
propose
term
named
coefficient
structure
node.
Further,
an
efficient
effective
Adaptive
Neighbourhood
Coefficient
Algorithm
(Adeff),
Extensive
experiments
real-world
datasets
demonstrate
effectiveness
efficiency
our
methods.
Compared
state-of-the-art
approach,
methods
achieve
up
450%
improvement
terms
effectiveness.
Mathematics,
Journal Year:
2024,
Volume and Issue:
12(7), P. 1041 - 1041
Published: March 30, 2024
Influence
maximization
(IM)
has
shown
wide
applicability
in
various
fields
over
the
past
few
decades,
e.g.,
viral
marketing,
rumor
control,
and
prevention
of
infectious
diseases.
Nevertheless,
existing
research
on
IM
primarily
focuses
ordinary
networks
with
pairwise
connections
between
nodes,
which
fall
short
representation
higher-order
relations.
hypergraphs
(HIM)
received
limited
attention.
A
novel
evaluation
function,
aims
to
evaluate
spreading
influence
selected
nodes
hypergraphs,
i.e.,
expected
diffusion
value
hypergraph
(HEDV),
is
proposed
this
work.
Then,
an
advanced
greedy-based
algorithm,
termed
HEDV-greedy,
select
seed
maximum
hypergraph.
We
conduct
extensive
experiments
eight
real-world
datasets,
benchmarking
HEDV-greedy
against
state-of-the-art
methods
for
HIM
problem.
Extensive
conducted
datasets
highlight
effectiveness
efficiency
our
methods.
The
algorithm
demonstrates
a
marked
reduction
time
complexity
by
two
orders
magnitude
compared
conventional
greedy
method.
Moreover,
outperforms
other
algorithms
across
all
datasets.
Specifically,
under
conditions
lower
propagation
probability,
exhibits
average
improvement
solution
accuracy
25.76%.
Entropy,
Journal Year:
2023,
Volume and Issue:
25(9), P. 1263 - 1263
Published: Aug. 25, 2023
Hypergraphs
have
become
an
accurate
and
natural
expression
of
high-order
coupling
relationships
in
complex
systems.
However,
applying
information
from
networks
to
vital
node
identification
tasks
still
poses
significant
challenges.
This
paper
proposes
a
von
Neumann
entropy-based
hypergraph
method
(HVC)
that
integrates
as
well
its
optimized
version
(semi-SAVC).
HVC
is
based
on
the
line
graph
structure
hypergraphs
measures
changes
network
complexity
using
entropy.
It
s-line
quantify
importance
by
mapping
hyperedges
nodes.
In
contrast,
semi-SAVC
uses
quadratic
approximation
entropy
measure
considers
only
half
maximum
order
hypergraph's
balance
accuracy
efficiency.
Compared
baseline
methods
hyperdegree
centrality,
closeness
vector
sub-hypergraph
new
demonstrated
superior
nodes
promote
influence
maintain
connectivity
empirical
data,
considering
robustness
factors.
The
correlation
monotonicity
results
were
quantitatively
analyzed
comprehensive
experimental
demonstrate
superiority
methods.
At
same
time,
key
non-trivial
phenomenon
was
discovered:
does
not
increase
linearly
orders
increase.
We
call
this
saturation
effect
identification.
When
reaches
value,
addition
often
acts
noise
affects
propagation.
Chaos An Interdisciplinary Journal of Nonlinear Science,
Journal Year:
2024,
Volume and Issue:
34(2)
Published: Feb. 1, 2024
Influence
maximization
problem
has
received
significant
attention
in
recent
years
due
to
its
application
various
domains,
such
as
product
recommendation,
public
opinion
dissemination,
and
disease
propagation.
This
paper
proposes
a
theoretical
analysis
framework
for
collective
influence
hypergraphs,
focusing
on
identifying
set
of
seeds
that
maximize
threshold
models.
First,
we
extend
the
message
passing
method
from
pairwise
networks
hypergraphs
accurately
describe
activation
process
Then,
introduce
concept
hypergraph
(HCI)
measure
nodes.
Subsequently,
design
an
algorithm,
HCI-TM,
select
set,
taking
into
account
both
node
hyperedge
activation.
Numerical
simulations
demonstrate
HCI-TM
outperforms
several
competing
algorithms
synthetic
real-world
hypergraphs.
Furthermore,
find
HCI
can
be
used
tool
predict
occurrence
cascading
phenomena.
Notably,
algorithm
works
better
larger
average
hyperdegrees
Erdös–Rényi
smaller
power-law
exponents
scale-free
