Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2024,
Номер
34(10)
Опубликована: Окт. 1, 2024
Real-world
complex
systems
demonstrated
temporal
features,
i.e.,
the
network
topology
varies
with
time
and
should
be
described
as
networks
since
traditional
static
cannot
accurately
characterize.
To
describe
deliberate
attack
events
in
networks,
we
propose
an
activity-based
targeted
on
activity-driven
to
investigate
networks’
percolation
properties
resilience.
Based
node
activity
mapping
framework,
giant
component
threshold
are
solved
according
theory
generating
function.
The
theoretical
results
coincide
simulation
near
thresholds.
We
find
that
attacks
can
affect
network,
while
random
cannot.
As
probability
of
a
highly
active
being
deleted
increases,
thus
enhancing
robustness.
When
network’s
distribution
is
extremely
heterogeneous,
robustness
decreases
consequently.
These
findings
help
us
analyze
understand
real-world
networks.
EPL (Europhysics Letters),
Год журнала:
2024,
Номер
147(1), С. 11002 - 11002
Опубликована: Июль 1, 2024
Abstract
Network
percolation
is
one
of
the
core
topics
in
network
science,
especially
understanding
and
optimizing
robustness
real-world
networks.
As
a
powerful
tool,
message-passing
approach
shows
unique
advantages
characterizing
compared
with
mean-field
approach.
This
simulates
behavioural
response
when
damaged
by
transmitting
updating
messages
between
nodes,
thereby
accurately
assessing
network.
paper
reviews
progress
approaches
on
simple
networks,
multilayer
networks
higher-order
recent
years
discusses
application
this
other
research
fields.
Finally,
we
discuss
future
directions
around
Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2024,
Номер
34(10)
Опубликована: Окт. 1, 2024
Real-world
complex
systems
demonstrated
temporal
features,
i.e.,
the
network
topology
varies
with
time
and
should
be
described
as
networks
since
traditional
static
cannot
accurately
characterize.
To
describe
deliberate
attack
events
in
networks,
we
propose
an
activity-based
targeted
on
activity-driven
to
investigate
networks’
percolation
properties
resilience.
Based
node
activity
mapping
framework,
giant
component
threshold
are
solved
according
theory
generating
function.
The
theoretical
results
coincide
simulation
near
thresholds.
We
find
that
attacks
can
affect
network,
while
random
cannot.
As
probability
of
a
highly
active
being
deleted
increases,
thus
enhancing
robustness.
When
network’s
distribution
is
extremely
heterogeneous,
robustness
decreases
consequently.
These
findings
help
us
analyze
understand
real-world
networks.
