Nature Communications,
Год журнала:
2025,
Номер
16(1)
Опубликована: Фев. 6, 2025
Cascades
are
self-reinforcing
processes
underlying
the
systemic
risk
of
many
complex
systems.
Understanding
universal
aspects
these
phenomena
is
fundamental
interest,
yet
typically
bound
to
numerical
observations
in
ad-hoc
models
and
limited
insights.
Here,
we
develop
a
unifying
approach
that
reveals
two
distinct
universality
classes
cascades
determined
by
global
symmetry
cascading
process.
We
provide
hyperscaling
arguments
predicting
hybrid
critical
characterized
combination
both
mean-field
spinodal
exponents
d-dimensional
corrections,
show
how
parity
invariance
influences
geometry
lifetime
avalanches.
Our
theory
applies
wide
range
networked
systems
arbitrary
dimensions,
as
demonstrate
simulations
encompassing
classic
novel
cascade
models,
revealing
principles
amenable
experimental
validation.
Cascading
accompanied
mixed-order
transitions
characteristic
for
ecological,
financial,
earth,
climate,
social
To
better
understand
mechanisms
behind,
authors
uncover
cascades,
symmetries
Journal of The Royal Society Interface,
Год журнала:
2022,
Номер
19(188)
Опубликована: Март 1, 2022
Network
science
has
evolved
into
an
indispensable
platform
for
studying
complex
systems.
But
recent
research
identified
limits
of
classical
networks,
where
links
connect
pairs
nodes,
to
comprehensively
describe
group
interactions.
Higher-order
a
link
can
more
than
two
have
therefore
emerged
as
new
frontier
in
network
science.
Since
interactions
are
common
social,
biological
and
technological
systems,
higher-order
networks
recently
led
important
discoveries
across
many
fields
research.
Here,
we
review
these
works,
focusing
particular
on
the
novel
aspects
dynamics
that
emerges
networks.
We
cover
variety
dynamical
processes
thus
far
been
studied,
including
different
synchronization
phenomena,
contagion
processes,
evolution
cooperation
consensus
formation.
also
outline
open
challenges
promising
directions
future
Journal of Neuroscience,
Год журнала:
2023,
Номер
43(34), С. 5989 - 5995
Опубликована: Авг. 23, 2023
The
brain
is
a
complex
system
comprising
myriad
of
interacting
elements,
posing
significant
challenges
in
understanding
its
structure,
function,
and
dynamics.
Network
science
has
emerged
as
powerful
tool
for
studying
such
intricate
systems,
offering
framework
integrating
multiscale
data
complexity.
Here,
we
discuss
the
application
network
study
brain,
addressing
topics
models
metrics,
connectome,
role
dynamics
neural
networks.
We
explore
opportunities
multiple
streams
transitions
from
development
to
healthy
function
disease,
potential
collaboration
between
neuroscience
communities.
underscore
importance
fostering
interdisciplinary
through
funding
initiatives,
workshops,
conferences,
well
supporting
students
postdoctoral
fellows
with
interests
both
disciplines.
By
uniting
communities,
can
develop
novel
network-based
methods
tailored
circuits,
paving
way
towards
deeper
functions.
IEEE Transactions on Signal Processing,
Год журнала:
2024,
Номер
72, С. 4745 - 4781
Опубликована: Янв. 1, 2024
Filters
are
fundamental
in
extracting
information
from
data.
For
time
series
and
image
data
that
reside
on
Euclidean
domains,
filters
the
crux
of
many
signal
processing
machine
learning
techniques,
including
convolutional
neural
networks.
Increasingly,
modern
also
networks
other
irregular
domains
whose
structure
is
better
captured
by
a
graph.
To
process
learn
such
data,
graph
account
for
underlying
domain.
In
this
article,
we
provide
comprehensive
overview
filters,
different
filtering
categories,
design
strategies
each
type,
trade-offs
between
types
filters.
We
discuss
how
to
extend
into
filter
banks
enhance
representational
power;
is,
model
broader
variety
classes,
patterns,
relationships.
showcase
role
applications.
Our
aim
article
provides
unifying
framework
both
beginner
experienced
researchers,
as
well
common
understanding
promotes
collaborations
at
intersections
processing,
learning,
application
domains.
Physical review. E,
Год журнала:
2024,
Номер
109(1)
Опубликована: Янв. 17, 2024
Hypergraphs
capture
the
higher-order
interactions
in
complex
systems
and
always
admit
a
factor
graph
representation,
consisting
of
bipartite
network
nodes
hyperedges.
As
hypegraphs
are
ubiquitous,
investigating
hypergraph
robustness
is
problem
major
research
interest.
In
literature
hypergraphs
so
far
only
has
been
treated
adopting
factor-graph
percolation,
which
describes
well
remain
functional
even
after
removal
one
more
their
nodes.
This
approach,
however,
fall
short
to
describe
situations
fail
when
any
removed,
this
latter
scenario
applying,
for
instance,
supply
chains,
catalytic
networks,
protein-interaction
networks
chemical
reactions,
etc.
Here
we
show
that
these
cases
correct
process
investigate
with
distinct
from
percolation.
We
build
message-passing
theory
its
critical
behavior
using
generating
function
formalism
supported
by
Monte
Carlo
simulations
on
random
real
data.
Notably,
node
percolation
threshold
exceeds
graphs.
Furthermore
differently
what
happens
ordinary
graphs,
hyperedge
do
not
coincide,
exceeding
threshold.
These
results
demonstrate
fat-tailed
cardinality
distribution
hyperedges
cannot
lead
hyper-resilience
phenomenon
contrast
where
divergent
second
moment
guarantees
zero
Physical review. E,
Год журнала:
2022,
Номер
106(3)
Опубликована: Сен. 26, 2022
Hypergraphs
and
simplical
complexes
both
capture
the
higher-order
interactions
of
complex
systems,
ranging
from
collaboration
networks
to
brain
networks.
One
open
problem
in
field
is
what
should
drive
choice
adopted
mathematical
framework
describe
starting
data
interactions.
Unweighted
simplicial
typically
involve
a
loss
information
data,
though
having
benefit
topology
data.
In
this
work
we
show
that
weighted
allow
circumvent
all
limitations
unweighted
represent
particular,
can
without
information,
allowing
at
same
time
The
probed
by
studying
spectral
properties
suitably
defined
Hodge
Laplacians
displaying
normalized
spectrum.
spectrum
(weighted)
here
studied
combining
cohomology
theory
with
theory.
proposed
framework,
quantify
compare
content
spectra
different
dimension
using
entropies
relative
entropies.
methodology
tested
on
real
version
model
"Network
Geometry
Flavor".
