This is a table of type bigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
bigram | frequency |
---|---|
social networks | 373 |
neural networks | 361 |
social network | 321 |
neural network | 300 |
social media | 290 |
network structure | 257 |
complex networks | 242 |
machine learning | 212 |
community structure | 191 |
network analysis | 186 |
infectious diseases | 160 |
epidemic threshold | 149 |
infectious disease | 146 |
temporal networks | 127 |
deep learning | 125 |
degree distribution | 122 |
node i | 118 |
big data | 118 |
disruption propagation | 114 |
cord uid | 113 |
complex network | 113 |
doc id | 113 |
contact networks | 108 |
network model | 105 |
epidemic spreading | 101 |
total number | 98 |
protein interaction | 95 |
data set | 95 |
contact network | 94 |
betweenness centrality | 93 |
wearing condition | 92 |
supply chain | 92 |
protein interactions | 90 |
systemic risk | 90 |
disease transmission | 90 |
community detection | 89 |
network inference | 88 |
gene expression | 87 |
data streams | 87 |
random network | 85 |
large number | 85 |
time step | 84 |
public health | 82 |
network representation | 82 |
network models | 82 |
adjacency matrix | 82 |
sr network | 80 |
infected individuals | 80 |
network theory | 79 |
infected nodes | 79 |
different types | 79 |
convolutional neural | 78 |
world networks | 77 |
power law | 76 |
network size | 76 |
condition identification | 74 |
average degree | 72 |
social distancing | 71 |
free networks | 71 |
type edges | 70 |
random networks | 70 |
network topology | 70 |
input data | 70 |
representation learning | 66 |
road network | 66 |
respiratory syndrome | 65 |
virus propagation | 63 |
link prediction | 63 |
disease spreading | 62 |
entrepreneurial entry | 62 |
network structures | 62 |
data sets | 62 |
output layer | 62 |
surface roughness | 61 |
acute respiratory | 61 |
human brain | 61 |
disease dynamics | 61 |
data analysis | 60 |
drug discovery | 59 |
transmission rate | 59 |
multilayer networks | 57 |
epidemic models | 57 |
time series | 57 |
public research | 57 |
one health | 57 |
path length | 57 |
interaction networks | 57 |
ml based | 56 |
network ties | 55 |
tail risk | 54 |
immunization strategies | 54 |
sis model | 53 |
two nodes | 53 |
severe acute | 53 |
ppi network | 53 |
shannon entropy | 53 |
disease outbreaks | 53 |
clustering coefficient | 53 |
data stream | 52 |
trust propensity | 52 |
media use | 52 |
world network | 51 |
widely used | 51 |
network dynamics | 50 |
temporal network | 50 |
authors proposed | 50 |
real world | 49 |
infection rate | 49 |
random walk | 49 |
random graph | 48 |
rights reserved | 48 |
free network | 48 |
multiplex networks | 48 |
small number | 48 |
preventive behavior | 47 |
entrepreneurial orientation | 47 |
inference methods | 47 |
case study | 47 |
aconitine alkaloids | 46 |
data mining | 46 |
immunization strategy | 46 |
traditional medicine | 46 |
sir model | 45 |
epidemic model | 45 |
social capital | 45 |
deep neural | 45 |
guarantee network | 45 |
surface science | 45 |
monte carlo | 44 |
online social | 44 |
ncov sars | 44 |
backward disruption | 44 |
innovation system | 43 |
interaction data | 43 |
global networks | 43 |
convolutional layers | 43 |
highly connected | 43 |
mental health | 42 |
offline social | 42 |
input layer | 42 |
united states | 42 |
energy consumption | 42 |
immune response | 41 |
two different | 41 |
systems medicine | 41 |
whole network | 41 |
epidemic dynamics | 41 |
allows us | 41 |
stock market | 40 |
also used | 40 |
results show | 40 |
steady state | 40 |
reproduction number | 40 |
infected individual | 40 |
sensor networks | 40 |
coronavirus disease | 40 |
object detection | 40 |
opportunistic routing | 40 |
data collection | 40 |
viral infection | 39 |
random walks | 39 |
closeness centrality | 39 |
small world | 39 |
dynamical processes | 39 |
shortest path | 39 |
recent years | 39 |
research institutions | 39 |
weak ties | 39 |
expression data | 38 |
probability distribution | 38 |
risk network | 38 |
giant component | 38 |
vaccination strategies | 38 |
systems biology | 38 |
degree distributions | 38 |
shortest paths | 37 |
based approach | 37 |
networks using | 37 |
experimental data | 37 |
hidden layers | 37 |
latent space | 37 |
network science | 37 |
interaction network | 37 |
social interactions | 36 |
cloud computing | 36 |
static networks | 36 |
wireless sensor | 36 |
information content | 36 |
information diffusion | 36 |
biological networks | 36 |
may also | 36 |
law distribution | 36 |
spreading dynamics | 36 |
heterogeneous networks | 35 |
artificial neural | 35 |
artificial intelligence | 35 |
wetting properties | 35 |
mutual information | 35 |
susceptible nodes | 35 |
network approach | 35 |
academic performance | 34 |
susceptible individuals | 34 |
microblogging marketing | 34 |
future research | 34 |
epidemic spread | 34 |
contact angle | 34 |
random graphs | 34 |
social systems | 34 |
i i | 34 |
omics data | 34 |
based network | 34 |
even though | 34 |
eighteenth century | 33 |
influential nodes | 33 |
average number | 33 |
closely related | 33 |
human protein | 33 |
visual analytics | 33 |
statistical physics | 33 |
network data | 33 |
high default | 33 |
network pharmacology | 33 |
contact tracing | 33 |
supply network | 32 |
learning methods | 32 |
nan doi | 32 |
connected nodes | 32 |
training data | 32 |
decision making | 32 |
least one | 32 |
learning techniques | 32 |
financial return | 31 |
relationships among | 31 |
basic reproduction | 31 |
domain interactions | 31 |
colloidal crystals | 31 |
verified users | 31 |
routing protocol | 31 |
time scale | 31 |
identification network | 31 |
disease spread | 31 |
innovation systems | 31 |
real networks | 30 |
table shows | 30 |
randomly chosen | 30 |
large networks | 30 |
drug targets | 30 |
business network | 30 |
dynamic networks | 30 |
social structure | 30 |
three different | 30 |
email network | 30 |
multilayer network | 30 |
rough net | 30 |
transfer learning | 30 |
complex systems | 30 |
markov chain | 30 |
based model | 30 |
one hand | 30 |
hidden layer | 29 |
based models | 29 |
iot objects | 29 |
population size | 29 |
crisis management | 29 |
od airport | 29 |
social interaction | 29 |
different networks | 29 |
based approaches | 29 |
high degree | 29 |
dynamic network | 29 |
supervised learning | 29 |
granular material | 29 |
cognitive distance | 29 |
simulation results | 29 |
traditional knowledge | 29 |
iuu fishing | 28 |
synthetic networks | 28 |
human proteins | 28 |
novel coronavirus | 28 |
relay node | 28 |
phase transitions | 28 |
recovery rate | 28 |
important role | 28 |
social contact | 28 |
node degree | 28 |
time point | 28 |
disease control | 28 |
absorbing state | 28 |
average path | 28 |
social scientists | 27 |
see also | 27 |
metapopulation network | 27 |
tipping points | 27 |
experimental results | 27 |
topological structure | 27 |
risk spillover | 27 |
training set | 27 |
research network | 27 |
droplet clusters | 27 |
meta path | 27 |
social relationships | 27 |
previous studies | 27 |
loss function | 27 |
smart contracts | 27 |
world health | 27 |
expression profiles | 27 |
peer review | 27 |
immune system | 27 |
different network | 27 |
also known | 27 |
network properties | 27 |
first step | 27 |
target data | 27 |
spoke network | 26 |
differential equations | 26 |
results obtained | 26 |
hospitality firms | 26 |
hm model | 26 |
law degree | 26 |
vaccination coverage | 26 |
technology resources | 26 |
fully connected | 26 |
social isolation | 26 |
bayesian inference | 26 |
wide range | 26 |
targeted immunization | 26 |
future work | 26 |
transmission probability | 26 |
ripple effect | 26 |
network connectivity | 26 |
energy efficiency | 26 |
sovereign bonds | 26 |
gene networks | 26 |
surveillance systems | 26 |
epidemiological data | 26 |
degree centrality | 26 |
blockchain systems | 25 |
anomaly detection | 25 |
quantized networks | 25 |
social networking | 25 |
molecular interaction | 25 |
clinical data | 25 |
health organization | 25 |
adaptive networks | 25 |
human disease | 25 |
drug combinations | 25 |
contact patterns | 25 |
infectious individuals | 25 |
infected node | 25 |
network topologies | 25 |
starting point | 25 |
data types | 25 |
breast cancer | 25 |
validated network | 25 |
social science | 25 |
virus infection | 25 |
epidemic thresholds | 25 |
latent period | 24 |
two layers | 24 |
strong ties | 24 |
business performance | 24 |
varying networks | 24 |
scaling relationships | 24 |
creative commons | 24 |
degree sequence | 24 |
study assistance | 24 |
growth rate | 24 |
coupling nodes | 24 |
critical point | 24 |
host proteins | 24 |
randomly selected | 24 |
social big | 24 |
two types | 24 |
two networks | 24 |
results showed | 24 |
learning algorithms | 24 |
business networks | 24 |
financial markets | 24 |
competitive advantage | 24 |
largest eigenvalue | 24 |
one another | 24 |
loan guarantee | 24 |
will also | 24 |
data packet | 23 |
training process | 23 |
see table | 23 |
image classification | 23 |
blockchain networks | 23 |
susceptible node | 23 |
objective measures | 23 |
materials science | 23 |
social contacts | 23 |
emotional profiles | 23 |
time scales | 23 |
information entropy | 23 |
phase transition | 23 |
spreading process | 23 |
social influence | 23 |
human social | 23 |
filtering methods | 23 |
conducting polymer | 23 |
among others | 23 |
genomic data | 23 |
repurposable drugs | 23 |
networks epidemic | 23 |
dynamic environment | 23 |
information processing | 23 |
syndrome coronavirus | 23 |
nan sha | 23 |
correlation coefficient | 23 |
epidemic outbreaks | 23 |
spreading processes | 23 |
structural efficiency | 23 |
business model | 23 |
connected component | 22 |
standard deviation | 22 |
iot devices | 22 |
human behavior | 22 |
clinical trials | 22 |
original network | 22 |
information regarding | 22 |
right wing | 22 |
colloidal systems | 22 |
skip connections | 22 |
networks based | 22 |
wavelet based | 22 |
preferential attachment | 22 |
protein networks | 22 |
emotional profile | 22 |
mathematical models | 22 |
network using | 22 |
gene network | 22 |
network security | 22 |
data sources | 22 |
dynamical systems | 22 |
transmission tree | 22 |
supply chains | 22 |
information flow | 22 |
critical value | 22 |
global change | 22 |
publicly available | 22 |
pandemic influenza | 21 |
recent studies | 21 |
network reconstruction | 21 |
scaling laws | 21 |
disease progression | 21 |
default mode | 21 |
learning model | 21 |
facial recognition | 21 |
game theory | 21 |
diffusion process | 21 |
sierra leone | 21 |
high level | 21 |
human interactome | 21 |
based denoising | 21 |
also found | 21 |
directly connected | 21 |
may lead | 21 |
activation function | 21 |
homogeneous mixing | 21 |
epidemic size | 21 |
proteins targeted | 21 |
phase i | 21 |
commonly used | 21 |
relatively small | 21 |
shape model | 21 |
using network | 21 |
conceptual framework | 21 |
drone detection | 21 |
free behavior | 20 |
network based | 20 |
european countries | 20 |
large scale | 20 |
three parts | 20 |
parameter values | 20 |
threshold value | 20 |
different levels | 20 |
topological properties | 20 |
interaction structure | 20 |
based methods | 20 |
air cargo | 20 |
protein complexes | 20 |
individuals may | 20 |
edge betweenness | 20 |
network training | 20 |
port community | 20 |
deconvolutional layers | 20 |
authors also | 20 |
proposed algorithm | 20 |
deep convolutional | 20 |
multiplex network | 20 |
open source | 20 |
influential spreaders | 20 |
node vulnerability | 20 |
diffusion processes | 20 |
entire network | 20 |
dynamical process | 20 |
remote areas | 20 |
response network | 20 |
interconnected nodes | 20 |
path detection | 20 |
ray images | 20 |
social discourse | 20 |
emotional profiling | 20 |
generative adversarial | 20 |
group size | 20 |
label propagation | 20 |
simplicial complexes | 20 |
global network | 20 |
bipartite network | 20 |
care utilization | 20 |
network density | 20 |
table i | 20 |
network embedding | 20 |
nearest neighbors | 20 |
gene co | 19 |
colloidal science | 19 |
airport pairs | 19 |
virtual robot | 19 |
integrated networks | 19 |
desired output | 19 |
activation functions | 19 |
email networks | 19 |
transfer function | 19 |
death rate | 19 |
better understanding | 19 |
collective dynamics | 19 |
ebola virus | 19 |
numerical simulations | 19 |
differential expression | 19 |
use cases | 19 |
influenza pandemic | 19 |
contagion processes | 19 |
literature review | 19 |
composite materials | 19 |
sovereign bond | 19 |
disruption diffusion | 19 |
discrete time | 19 |
epidemic processes | 19 |
intensive care | 19 |
trained neural | 19 |
transport networks | 19 |
two models | 19 |
natural language | 19 |
long time | 19 |
human contact | 19 |
friendship network | 19 |
apply machine | 19 |
private sphere | 19 |
source node | 19 |
model complex | 19 |
connected individuals | 19 |
drug repurposing | 19 |
airport network | 19 |
free energy | 19 |
base station | 19 |
control charts | 19 |
behavior dynamics | 19 |
chain network | 19 |
section presents | 19 |
structural properties | 19 |
various network | 19 |
demographic cues | 19 |
key role | 19 |
every node | 19 |
bond yields | 19 |
research networks | 19 |
continuous time | 19 |
bottle type | 19 |
one node | 19 |
control strategies | 19 |
ann models | 19 |
ocean acidification | 18 |
computer models | 18 |
health status | 18 |
per unit | 18 |
better performance | 18 |
networks will | 18 |
two distinct | 18 |
recently suggested | 18 |
average shortest | 18 |
random forest | 18 |
several stages | 18 |
knowledge systems | 18 |
across different | 18 |
unit time | 18 |
emerging infectious | 18 |
bond cluster | 18 |
social sciences | 18 |
knowledge acquired | 18 |
air travel | 18 |
raw data | 18 |
mainly expressed | 18 |
regular network | 18 |
clustered networks | 18 |
wearing masks | 18 |
focal firm | 18 |
previous section | 18 |
highly empirical | 18 |
represent complex | 18 |
graph theory | 18 |
supplementary material | 18 |
based systems | 18 |
network may | 18 |
interdisciplinary area | 18 |
basic reproductive | 18 |
centrality measures | 18 |
temporal dynamics | 18 |
additional information | 18 |
reference model | 18 |
densely connected | 18 |
network will | 18 |
worth noting | 18 |
carbon nanotube | 18 |
connectivity patterns | 18 |
networks date | 18 |
aboav scaling | 18 |
big amount | 18 |
previous work | 18 |
organizational networks | 18 |
social brain | 18 |
risk management | 18 |
forwarder list | 18 |
course corrections | 18 |
social contagion | 18 |
fake news | 18 |
layers connect | 18 |
learning approach | 17 |
examining individual | 17 |
real data | 17 |
tribology remains | 17 |
transition probability | 17 |
activations based | 17 |
ductile iron | 17 |
composite including | 17 |
various types | 17 |
deconvolutional layer | 17 |
ann model | 17 |
small rigid | 17 |
predict surface | 17 |
new outcomes | 17 |
biological processes | 17 |
better understand | 17 |
inductive science | 17 |
network architectures | 17 |
material composition | 17 |
repellent properties | 17 |
spectral radius | 17 |
network perspective | 17 |
multilayer perception | 17 |
optimized design | 17 |
models learn | 17 |
droplets used | 17 |
electrical conducting | 17 |
complex dependencies | 17 |
test set | 17 |
knowledge structure | 17 |
regular grid | 17 |
physical principles | 17 |
since ann | 17 |
retrieving acquired | 17 |
mode network | 17 |
tribological studies | 17 |
hierarchical structure | 17 |
correlations allows | 17 |
user interface | 17 |
drug design | 17 |
anns incorporate | 17 |
compartmental models | 17 |
crystals made | 17 |
various materials | 17 |
proposed protocol | 17 |
engineering components | 17 |
varying connection | 17 |
synaptic weights | 17 |
surface free | 17 |
driven inductive | 17 |
metallic composite | 17 |
acquired knowledge | 17 |
central node | 17 |
baseline shifts | 17 |
resembling neural | 17 |
based analysis | 17 |
image pre | 17 |
surface wetting | 17 |
madrazo madrazo | 17 |
somewhat resembling | 17 |
high quality | 17 |
learning task | 17 |
wear rate | 17 |
finally delivered | 17 |
novel hydrophobic | 17 |
surface scientists | 17 |
end delay | 17 |
anns learn | 17 |
levitating droplet | 17 |
output relationships | 17 |
complex input | 17 |
exponential growth | 17 |
human diseases | 17 |
become infected | 17 |
organizational field | 17 |
data points | 17 |
including complex | 17 |
contacting surfaces | 17 |
training makes | 17 |
engineers often | 17 |
network construction | 17 |
surface properties | 17 |
predicting water | 17 |
called tribology | 17 |
allows predicting | 17 |
graph models | 17 |
metapopulation networks | 17 |
degree nodes | 17 |
local clustering | 17 |
making adjustments | 17 |
information network | 17 |
empirical data | 17 |
different ways | 17 |
connections leading | 17 |
perception neural | 17 |
mixed reality | 17 |
learning rate | 17 |
connection weights | 17 |
network architecture | 17 |
layer compute | 17 |
graphite composite | 17 |
linear transfer | 17 |
superhydrophobic materials | 17 |
objective function | 17 |
academic achievements | 17 |
first physical | 17 |
tribology deals | 17 |
strong community | 17 |
networks network | 17 |
units representing | 17 |
neighboring nodes | 17 |
two main | 17 |
focal firms | 17 |
resilience investment | 17 |
often deal | 17 |
convolutional networks | 17 |
another area | 17 |
rigid particles | 17 |
expression networks | 17 |
important scaling | 17 |
models somewhat | 17 |
typical ann | 17 |
network biology | 17 |
complex neurons | 17 |
interacting proteins | 17 |
movement data | 17 |
individual records | 17 |
dengue virus | 17 |
recurrent neural | 17 |
wetting experiments | 17 |
area closely | 17 |
water contact | 17 |
facial images | 17 |
edge computing | 17 |
predicting new | 17 |
applied branch | 17 |
nodal connections | 17 |
experimental manner | 17 |
two separate | 17 |
nineteenth century | 16 |
epidemiological models | 16 |
contact process | 16 |
opinion model | 16 |
layer i | 16 |
vice versa | 16 |
computational complexity | 16 |
next section | 16 |
sensor network | 16 |
networking ties | 16 |
prefrontal cortex | 16 |
edge weights | 16 |
road networks | 16 |
outgoing edges | 16 |
interpersonal social | 16 |
transfer rate | 16 |
crucial role | 16 |
early warning | 16 |
network edges | 16 |
pattern recognition | 16 |
computer vision | 16 |
connected network | 16 |
estimated using | 16 |
final size | 16 |
may provide | 16 |
ground truth | 16 |
spanning tree | 16 |
potential target | 16 |
node selection | 16 |
research performance | 16 |
polymer nanocomposite | 16 |
model parameters | 16 |
statistical analysis | 16 |
human dynamics | 16 |
total population | 16 |
subdiffusive process | 16 |
water level | 16 |
international organizations | 16 |
three networks | 16 |
real time | 16 |
will focus | 16 |
nodes within | 16 |
medical devices | 16 |
bibliometric analysis | 16 |
outbreak threshold | 16 |
disease outbreak | 16 |
many cases | 16 |
nodes i | 16 |
network characteristics | 16 |
sars coronavirus | 16 |
based techniques | 16 |
regulatory networks | 16 |
risk propagation | 16 |
asset returns | 16 |
acoustic signals | 16 |
social ties | 16 |
reinforcement learning | 16 |
proposed model | 16 |
transmission dynamics | 16 |
ashwagandha network | 15 |
expected number | 15 |
gene regulatory | 15 |
special path | 15 |
many different | 15 |
take place | 15 |
entrepreneurial process | 15 |
plasmodium falciparum | 15 |
research interests | 15 |
communication network | 15 |
transport network | 15 |
transmission trees | 15 |
molecular interactions | 15 |
future studies | 15 |
peak stage | 15 |
two kinds | 15 |
data collected | 15 |
public sphere | 15 |
hoc networks | 15 |
graph model | 15 |
mixed population | 15 |
average connectivity | 15 |
simple slope | 15 |
expression network | 15 |
global health | 15 |
patient zero | 15 |
state actors | 15 |
control measures | 15 |
average time | 15 |
environmental dynamism | 15 |
adversarial networks | 15 |
international airport | 15 |
one focal | 15 |
misinformation spreading | 15 |
forward disruption | 15 |
takes place | 15 |
mathematical theory | 15 |
hierarchical network | 15 |
incubation period | 15 |
communication networks | 15 |
string database | 15 |
detection performance | 15 |
network proximity | 15 |
time period | 15 |
program year | 15 |
betweenness strategy | 15 |
propagation model | 15 |
ai ml | 15 |
nodes represent | 15 |
nodes refer | 15 |
infection spread | 15 |
sensor nodes | 15 |
long term | 15 |
data integration | 15 |
real network | 15 |
much smaller | 15 |
input image | 15 |
infectious period | 15 |
scientific collaboration | 15 |
computer science | 15 |
support vector | 15 |
also provides | 15 |
text mining | 15 |
chinese medicine | 15 |
different values | 15 |
local information | 15 |
research institutes | 15 |
network actors | 15 |
blockchain technology | 15 |
block model | 15 |
southern ocean | 15 |
quantized neural | 15 |
social bonds | 15 |
underlying network | 15 |
data science | 15 |
comparative analysis | 15 |
var thresholds | 15 |
viral infections | 15 |
traditional chinese | 15 |
occurrence networks | 15 |
contagion process | 15 |
financial networks | 15 |
transmission network | 15 |
collective behavior | 15 |
network filtering | 15 |
risk spillovers | 15 |
data packets | 15 |
new node | 15 |
augmented reality | 15 |
next step | 15 |
critical behavior | 15 |
much faster | 15 |
feature maps | 15 |
given time | 15 |
facial detection | 15 |
time required | 15 |
diffusion path | 15 |
vertex entity | 15 |
determine whether | 15 |
see methods | 15 |
less likely | 15 |
make use | 15 |
financial risk | 15 |
risk factors | 15 |
interactions among | 15 |
also use | 15 |
hand side | 15 |
granular materials | 14 |
recovery probability | 14 |
also called | 14 |
proposed approach | 14 |
correlation length | 14 |
undirected network | 14 |
vertex i | 14 |
acquaintance method | 14 |
law exponent | 14 |
twitter users | 14 |
systemically important | 14 |
proteins mainly | 14 |
attribute information | 14 |
topological features | 14 |
infection curve | 14 |
gene ontology | 14 |
interaction terms | 14 |
context data | 14 |
reproductive number | 14 |
network health | 14 |
vaccination strategy | 14 |
data processing | 14 |
systematic review | 14 |
using data | 14 |
node vec | 14 |
immunized nodes | 14 |
quantile regression | 14 |
adversarial attacks | 14 |
basole bellamy | 14 |
higher social | 14 |
boundary conditions | 14 |
power exponent | 14 |
family members | 14 |
functional edge | 14 |
functional modules | 14 |
sir epidemic | 14 |
window size | 14 |
voter model | 14 |
cohesive subgroups | 14 |
initial infected | 14 |
model based | 14 |
configuration model | 14 |
mass spectrometry | 14 |
bayesian networks | 14 |
wearing conditions | 14 |
upper bound | 14 |
hidden markov | 14 |
different layers | 14 |
line staff | 14 |
processing delay | 14 |
also provide | 14 |
post roads | 14 |
weighted networks | 14 |
radial structure | 14 |
mobile devices | 14 |
one way | 14 |
adaptive network | 14 |
engineered features | 14 |
positive correlations | 14 |
two classes | 14 |
learning method | 14 |
transmission pathways | 14 |
innate immune | 14 |
proposed method | 14 |
law networks | 14 |
subgraph centrality | 14 |
static network | 14 |
time steps | 14 |
normal distribution | 14 |
also shows | 14 |
total visits | 14 |
higher pagerank | 14 |
interactome network | 14 |
results indicate | 14 |
climate change | 14 |
horizontal network | 14 |
health care | 14 |
image super | 14 |
bridge symptoms | 14 |
human ppi | 14 |
expression level | 14 |
outbreak stage | 14 |
structural controllability | 14 |
based devices | 14 |
system size | 14 |
comparative study | 14 |
rank attack | 14 |
second neighbors | 14 |
gradient descent | 14 |
paved roads | 14 |
exponential random | 14 |
language processing | 14 |
learning approaches | 14 |
east respiratory | 13 |
middle east | 13 |
acas xu | 13 |
using different | 13 |
semantic information | 13 |
open access | 13 |
social connections | 13 |
highly clustered | 13 |
research questions | 13 |
meta paths | 13 |
significantly higher | 13 |
airport pair | 13 |
different immunization | 13 |
short time | 13 |
image processing | 13 |
spatial structure | 13 |
network associates | 13 |
body mass | 13 |
ego network | 13 |
protein pairs | 13 |
phylogenetic trees | 13 |
rural areas | 13 |
performed using | 13 |
large amount | 13 |
network performance | 13 |
first transformation | 13 |
feature engineering | 13 |
single shot | 13 |
invasion period | 13 |
transport costs | 13 |
firm performance | 13 |
critical care | 13 |
main text | 13 |
higher level | 13 |
three types | 13 |
statistical mechanics | 13 |
social group | 13 |
may cause | 13 |
expression profile | 13 |
new york | 13 |
contact rates | 13 |
leadership team | 13 |
detection algorithm | 13 |
performance diffusion | 13 |
modeling approach | 13 |
optical networks | 13 |
gene ly | 13 |
thermodynamic limit | 13 |
much larger | 13 |
research areas | 13 |
detected communities | 13 |
put forward | 13 |
epistemic communities | 13 |
italia viva | 13 |
network evolution | 13 |
dynamic social | 13 |
across multiple | 13 |
relay nodes | 13 |
based drug | 13 |
international conference | 13 |
case studies | 13 |
initial growth | 13 |
sentinel surveillance | 13 |
copyright holder | 13 |
driven networks | 13 |
detection using | 13 |
overall network | 13 |
time periods | 13 |
carlo simulations | 13 |
economic growth | 13 |
immune cells | 13 |
learning based | 13 |
environmental change | 13 |
final deconvolutional | 13 |
forwarder node | 13 |
missing values | 13 |
layer network | 13 |
generated using | 13 |
epidemiological network | 13 |
default risk | 13 |
collaboration networks | 13 |
hong kong | 13 |
ct data | 13 |
stochastic system | 13 |
poisson process | 13 |
cell expression | 13 |
selected nodes | 13 |
entrepreneurial firms | 13 |
default rates | 13 |
large social | 13 |
port authority | 13 |
wide variety | 13 |
clearly shows | 13 |
disease prevalence | 13 |
impact score | 13 |
higher centrality | 13 |
information sharing | 13 |
avian mycobacteriosis | 13 |
research papers | 13 |
final sizes | 13 |
theoretical framework | 13 |
transcriptomic data | 13 |
potential targets | 13 |
node will | 13 |
bridge centrality | 13 |
word networks | 13 |
path dependence | 13 |
using deep | 13 |
susceptible individual | 13 |
analysis using | 13 |
healthy individuals | 13 |
raw movement | 13 |
health services | 12 |
building blocks | 12 |
least squares | 12 |
allowed without | 12 |
among individuals | 12 |
regular lattice | 12 |
field theory | 12 |
multiple data | 12 |
interactions may | 12 |
molecular docking | 12 |
email viruses | 12 |
molecular mechanisms | 12 |
brain activity | 12 |
median age | 12 |
network medicine | 12 |
per second | 12 |
learning models | 12 |
following two | 12 |
best method | 12 |
air transport | 12 |
target network | 12 |
distancing measures | 12 |
epidemic process | 12 |
future directions | 12 |
based networks | 12 |
image recognition | 12 |
first two | 12 |
point cloud | 12 |
every time | 12 |
network research | 12 |
available data | 12 |
newly paved | 12 |
network centrality | 12 |
escherichia coli | 12 |
female baboons | 12 |
herd immunity | 12 |
computer network | 12 |
impaired motor | 12 |
becomes infected | 12 |
fractional diffusion | 12 |
global environmental | 12 |
psi mi | 12 |
small molecule | 12 |
persistent cycles | 12 |
signal processing | 12 |
short term | 12 |
epidemic outbreak | 12 |
convergence factor | 12 |
somewhat similar | 12 |
global spread | 12 |
colloidal clusters | 12 |
randomly generated | 12 |
social bonding | 12 |
major public | 12 |
interactions within | 12 |
formal verification | 12 |
valued network | 12 |
medical device | 12 |
networks may | 12 |
propagation behavior | 12 |
disease status | 12 |
social groups | 12 |
transportation network | 12 |
regression model | 12 |
new data | 12 |
contact structure | 12 |
two years | 12 |
hospitality industry | 12 |
avian influenza | 12 |
linear regression | 12 |
two cases | 12 |
may occur | 12 |
entity mask | 12 |
general facial | 12 |
seq profiles | 12 |
enrichment analysis | 12 |
missing data | 12 |
network epidemiology | 12 |
network information | 12 |
reuse allowed | 12 |
taking place | 12 |
mobile phone | 12 |
critical exponents | 12 |
bottle types | 12 |
heterogeneous biomedical | 12 |
network design | 12 |
cell rna | 12 |
peak stages | 12 |
general population | 12 |
without permission | 12 |
allow us | 12 |
incentive function | 12 |
fold change | 12 |
sample size | 12 |
concurrent neural | 12 |
current study | 12 |
testing set | 12 |
recent work | 12 |
high performance | 12 |
existing literature | 12 |
blockchain system | 12 |
structural holes | 12 |
respiratory illness | 12 |
brain networks | 12 |
commons license | 12 |
steel blue | 12 |
computationally efficient | 12 |
outbreak detection | 12 |
infected lung | 12 |
modern publication | 12 |
contour map | 12 |
low degree | 12 |
human service | 12 |
apa network | 12 |
lead apa | 12 |
different data | 12 |
negative correlation | 12 |
intrusion detection | 12 |
social dynamics | 12 |
batch size | 12 |
prediction model | 12 |
financial institutions | 12 |
ssd mobilenet | 12 |
maximum number | 12 |
lead network | 12 |
based modeling | 12 |
computer networks | 12 |
management team | 12 |
two parts | 12 |
facial image | 12 |
new infections | 12 |
blockchain network | 12 |
global epidemic | 12 |
fitted curve | 12 |
influencing factors | 12 |
non reputable | 12 |
enron email | 12 |
social support | 12 |
sexually transmitted | 12 |
policy makers | 12 |
will show | 12 |
biological systems | 12 |
stereo matching | 12 |
secret sharing | 12 |
financial system | 12 |
heterogeneous network | 12 |
gated channel | 12 |
betweenness immunization | 12 |
different methods | 12 |
new roads | 12 |
world data | 12 |
multiple types | 12 |
epidemic disease | 12 |
empirical studies | 12 |
early detection | 12 |
smart business | 12 |
infection network | 12 |
highly cited | 12 |
transmission events | 12 |
nodes will | 12 |
information dissemination | 12 |
wireless networks | 12 |
new approach | 12 |
posterior distribution | 12 |
recent advances | 12 |
disease genes | 12 |
useful tool | 12 |
authors declare | 12 |
will increase | 12 |
present study | 11 |
also find | 11 |
vast majority | 11 |
carlo simulation | 11 |
first time | 11 |
care data | 11 |
two approaches | 11 |
two major | 11 |
sensitivity analysis | 11 |
section describes | 11 |
infected neighbors | 11 |
contagion dynamics | 11 |
community structures | 11 |
infection propagator | 11 |
data available | 11 |
field model | 11 |
better results | 11 |
structural hole | 11 |
relatively high | 11 |
seir model | 11 |
disease models | 11 |
expression values | 11 |
modularity coefficient | 11 |
new network | 11 |
expert knowledge | 11 |
network lifetime | 11 |
stochastic model | 11 |
siotpredict framework | 11 |
coupled oscillators | 11 |
removing hubs | 11 |
high clustering | 11 |
cropped images | 11 |
regime switching | 11 |
host interactions | 11 |
public events | 11 |
amla network | 11 |
jacobian matrix | 11 |
detection system | 11 |
quality improvement | 11 |
analytical model | 11 |
south korea | 11 |
clustering algorithm | 11 |
antwerp port | 11 |
author funder | 11 |
increasing number | 11 |
last decade | 11 |
publication standards | 11 |
infectious periods | 11 |
proposed selfrl | 11 |
granted medrxiv | 11 |
parameter characterizing | 11 |
channel type | 11 |
media data | 11 |
best results | 11 |
citation network | 11 |
informational content | 11 |
proteomic data | 11 |
allometric scaling | 11 |
larger social | 11 |
infective individuals | 11 |
policy network | 11 |
enrichment analyses | 11 |
recognition model | 11 |
molecular dynamics | 11 |
disease will | 11 |
treatment condition | 11 |
biological functions | 11 |
default groups | 11 |
shed light | 11 |
edge ratio | 11 |
bond configurations | 11 |
coupled disease | 11 |
will depend | 11 |
transcription factors | 11 |
secondary infections | 11 |
interactive email | 11 |
host cell | 11 |
stochastic algorithms | 11 |
biomedical research | 11 |
network sizes | 11 |
institutional affiliations | 11 |
feature extraction | 11 |
computer simulations | 11 |
physical contact | 11 |
policy actors | 11 |
identifi cation | 11 |
stationary state | 11 |
research institute | 11 |
health outcomes | 11 |
automated validation | 11 |
recent research | 11 |
series data | 11 |
consensus protocols | 11 |
brain size | 11 |
many real | 11 |
opinion leaders | 11 |
spanish road | 11 |
binary interactions | 11 |
four types | 11 |
adopt preventive | 11 |
current network | 11 |
network governance | 11 |
syndrome groups | 11 |
overlap among | 11 |
real robot | 11 |
diffusion rate | 11 |
diffusive processes | 11 |
network visualization | 11 |
mixed populations | 11 |
common alter | 11 |
cortical networks | 11 |
adaptive innovation | 11 |
cargo airlines | 11 |
unverified users | 11 |
existing methods | 11 |
authors found | 11 |
among genes | 11 |
hmf theory | 11 |
sf networks | 11 |
public sector | 11 |
batch normalization | 11 |
current status | 11 |
disease parameters | 11 |
small fraction | 11 |
clinical practice | 11 |
relation ratio | 11 |
include granular | 11 |
relative importance | 11 |
human mobility | 11 |
diffusion patterns | 11 |
creative outcomes | 11 |
hashtag networks | 11 |
relu activation | 11 |
multiple levels | 11 |
news articles | 11 |
falciparum malaria | 11 |
provide information | 11 |
healthcare services | 11 |
larger number | 11 |
based algorithm | 11 |
object detectors | 11 |
training dataset | 11 |
information propagation | 11 |
discourse around | 11 |
official accounts | 11 |
prevention measures | 11 |
also important | 11 |
network system | 11 |
th century | 11 |
sis dynamics | 11 |
driver nodes | 11 |
curve fitting | 11 |
based simulation | 11 |
single image | 11 |
multiple modalities | 11 |
drug repositioning | 11 |
randomly choose | 11 |
different epidemic | 11 |
positive effect | 11 |
cellular processes | 11 |
latency time | 11 |
net dataset | 11 |
percolation thresholds | 11 |
next time | 11 |
network regime | 11 |
vector machines | 11 |
assistance network | 11 |
based filtering | 11 |
information retrieval | 11 |
opensky network | 11 |
active nodes | 11 |
home care | 11 |
point clouds | 11 |
walk centrality | 11 |
financial experts | 11 |
edge weight | 11 |
information spreading | 11 |
airline network | 11 |
maximum value | 11 |
may help | 11 |
networks inferred | 11 |
van den | 11 |
large extent | 11 |
natural products | 11 |
transaction graph | 11 |
jaccard coefficient | 10 |
data using | 10 |
research collaboration | 10 |
often used | 10 |
principal component | 10 |
second neighbor | 10 |
statistically significant | 10 |
mean degree | 10 |
community networks | 10 |
global level | 10 |
stationary distribution | 10 |
three integrators | 10 |
chinese stock | 10 |
benchmark methods | 10 |
dataset used | 10 |
solver nodes | 10 |
among organizations | 10 |
decision support | 10 |
highly deg | 10 |
representation vectors | 10 |
positive correlation | 10 |
sis epidemic | 10 |
generative models | 10 |
fitting equation | 10 |
wifi networks | 10 |
empirical evidence | 10 |
deep residual | 10 |
data augmentation | 10 |
surface given | 10 |
side effects | 10 |
one time | 10 |
spurious counterexample | 10 |
random expectation | 10 |
i th | 10 |
probability density | 10 |
infectious node | 10 |
innovation network | 10 |
multimodal data | 10 |
close contacts | 10 |
regulatory network | 10 |
use case | 10 |
models may | 10 |
open issues | 10 |
atd vector | 10 |
biological network | 10 |
phase ii | 10 |
response networks | 10 |
one network | 10 |
two random | 10 |
based algorithms | 10 |
time evolution | 10 |
network used | 10 |
experimental design | 10 |
entropy approach | 10 |
medical masks | 10 |
input images | 10 |
influence maximization | 10 |
based informational | 10 |
gray matter | 10 |
negatively correlated | 10 |
see materials | 10 |
one particular | 10 |
pathogen genomic | 10 |
much less | 10 |
different scales | 10 |
generated networks | 10 |
malaria elimination | 10 |
incidence rate | 10 |
might lead | 10 |
epidemiological model | 10 |
experimental evidence | 10 |
kernel size | 10 |
statistical probability | 10 |
highest degree | 10 |
host interactome | 10 |
will need | 10 |
infection peak | 10 |
based walk | 10 |
based applications | 10 |
also show | 10 |
available capacity | 10 |
interaction partners | 10 |
functional connectivity | 10 |
complex system | 10 |
recovered individuals | 10 |
high levels | 10 |
circumplex model | 10 |
discovery network | 10 |
control state | 10 |
different approaches | 10 |
step towards | 10 |
certain threshold | 10 |
consensus protocol | 10 |
new venture | 10 |
inferred using | 10 |
simulation model | 10 |
dimensional space | 10 |
geographical proximity | 10 |
probability per | 10 |
heterogeneous information | 10 |
structural information | 10 |
siot network | 10 |
gene set | 10 |
evolving networks | 10 |
amino acid | 10 |
ebola response | 10 |
three main | 10 |
hc strategy | 10 |
target nodes | 10 |
large numbers | 10 |
particular case | 10 |
rate equations | 10 |
node delay | 10 |
information systems | 10 |
random neighbor | 10 |
another approach | 10 |
complete information | 10 |
genetic algorithm | 10 |
shard transactions | 10 |
another important | 10 |
transcription factor | 10 |
work will | 10 |
relevant information | 10 |
pathogen interactions | 10 |
inflammatory response | 10 |
first stage | 10 |
homogeneous network | 10 |
network stack | 10 |
markov chains | 10 |
informational approach | 10 |
th state | 10 |
march th | 10 |
high trust | 10 |
epithelial cells | 10 |
almost one | 10 |
optimization problem | 10 |
give rise | 10 |
scalefree networks | 10 |
posterior probability | 10 |
restricted hm | 10 |
slip transition | 10 |
parameter space | 10 |
common alters | 10 |
primary subspecialty | 10 |
roughness parameter | 10 |
one layer | 10 |
control condition | 10 |
bonded communities | 10 |
agent i | 10 |
seq profile | 10 |
temporal sequence | 10 |
data transmission | 10 |
email virus | 10 |
human networks | 10 |
results presented | 10 |
birth rate | 10 |
similar results | 10 |
bayesian network | 10 |
wetting transitions | 10 |
left side | 10 |
clinical trial | 10 |
research question | 10 |
supervised representation | 10 |
two decades | 10 |
stability analysis | 10 |
update rule | 10 |
function secret | 10 |
tensorflow object | 10 |
mean variance | 10 |
activity driven | 10 |
one type | 10 |
new ventures | 10 |
direct flights | 10 |
random node | 10 |
travel restrictions | 10 |
percolation threshold | 10 |
low connectivity | 10 |
tiny imagenet | 10 |
second block | 10 |
proposed srcnet | 10 |
identifying influential | 10 |
load balancing | 10 |
initially infected | 10 |
packet loss | 10 |
interaction effects | 10 |
incidence curves | 10 |
endemic state | 10 |
main focus | 10 |
fully correlated | 10 |
centrality metrics | 10 |
based homophily | 10 |
pearson correlation | 10 |
significant differences | 10 |
cathay pacific | 10 |
sodium voltage | 10 |
directed graph | 10 |
size effects | 10 |
characterizing informational | 10 |
cellular networks | 10 |
analysis shows | 10 |
following values | 10 |
developing countries | 10 |
pharmaceutical interventions | 10 |
longitudinal study | 10 |
routing algorithm | 10 |
also included | 10 |
will become | 10 |
different strategies | 10 |
international institutions | 10 |
threshold model | 10 |
european union | 10 |
near future | 10 |
false positive | 10 |
full network | 10 |
mediated dengue | 10 |
good results | 10 |
high accuracy | 10 |
different groups | 10 |
health research | 10 |
processing time | 10 |
another example | 10 |
quantized network | 10 |
metric graphs | 10 |
hoc network | 10 |
high centrality | 10 |
price competition | 10 |
synthetic data | 10 |
white matter | 10 |
interaction database | 10 |
data across | 10 |
critical role | 10 |
retweet network | 10 |
information available | 10 |
expression analyses | 10 |
systems pharmacology | 10 |
networks identifying | 9 |
surface matching | 9 |
malaria prevalence | 9 |
small particles | 9 |
resulting network | 9 |
also possible | 9 |
empirical research | 9 |
host protein | 9 |
configurations somewhat | 9 |
ppi networks | 9 |
uniformly distributed | 9 |
network actor | 9 |
masks dataset | 9 |
results suggest | 9 |
hole spanners | 9 |
financial support | 9 |
health emergency | 9 |
shallow resnet | 9 |
following chapter | 9 |
control vs | 9 |
normal diffusion | 9 |
transport geography | 9 |
square lattice | 9 |
first term | 9 |
vaccination behavior | 9 |
research community | 9 |
lu model | 9 |
centrality measurements | 9 |
facial areas | 9 |
structural features | 9 |
new zealand | 9 |
destination node | 9 |
block models | 9 |
life cycle | 9 |
also suggest | 9 |
infection risk | 9 |
author impact | 9 |
time frame | 9 |
last two | 9 |
differential equation | 9 |
predatory pricing | 9 |
degree correlation | 9 |
receptive field | 9 |
also characteristic | 9 |
rectified linear | 9 |
outbreak size | 9 |
contagious diseases | 9 |
choice architecture | 9 |
larger networks | 9 |
underlying structure | 9 |
nodes selected | 9 |
distinct social | 9 |
many countries | 9 |
expressed outside | 9 |
right side | 9 |
business models | 9 |
statistical distributions | 9 |
abstract network | 9 |
absorbing states | 9 |
wearable devices | 9 |
edges represent | 9 |
knowledge sharing | 9 |
different communities | 9 |
many applications | 9 |
time unit | 9 |
component size | 9 |
pld values | 9 |
risk transmission | 9 |
aircraft behavior | 9 |
spreading model | 9 |
metabolic networks | 9 |
node represents | 9 |
may result | 9 |
three major | 9 |
large data | 9 |
national institute | 9 |
dynamic evolution | 9 |
mixing patterns | 9 |
personal influence | 9 |
also considered | 9 |
methods based | 9 |
influenza vaccination | 9 |
rapid development | 9 |
occurrence network | 9 |
immunodeficiency virus | 9 |
royal decree | 9 |
ecological systems | 9 |
protein interactome | 9 |
technical parameters | 9 |
human capital | 9 |
multivariate control | 9 |
clinical effects | 9 |
identification accuracy | 9 |
lung cells | 9 |
distance entropy | 9 |
learning algorithm | 9 |
observed within | 9 |
approach based | 9 |
essential proteins | 9 |
various systems | 9 |
protective behavior | 9 |
member connectivity | 9 |
supplementary table | 9 |
information networks | 9 |
networks show | 9 |
shot object | 9 |
extremely high | 9 |
random variable | 9 |
among iot | 9 |
results also | 9 |
complete network | 9 |
relatively low | 9 |
aircraft type | 9 |
many challenges | 9 |
systems studied | 9 |
algorithm proposed | 9 |
computational methods | 9 |
higher value | 9 |
matrix factorization | 9 |
data will | 9 |
spatial distribution | 9 |
information technology | 9 |
directed networks | 9 |
low power | 9 |
efficient way | 9 |
organizational research | 9 |
sufficiently large | 9 |
net definition | 9 |
highly heterogeneous | 9 |
dimensional structure | 9 |
diverse set | 9 |
level self | 9 |
information flows | 9 |
simple model | 9 |
market structure | 9 |
identifi ed | 9 |
hospitality businesses | 9 |
intersectoral relations | 9 |
surveillance system | 9 |
stochastic process | 9 |
potential drug | 9 |
air canada | 9 |
community size | 9 |
human interactions | 9 |
direct neighbors | 9 |
will result | 9 |
time distribution | 9 |
three categories | 9 |
systems form | 9 |
data management | 9 |
international concern | 9 |
related work | 9 |
networks temporal | 9 |
computational analysis | 9 |
detection api | 9 |
cp model | 9 |
mathematical model | 9 |
higher degree | 9 |
manned aircraft | 9 |
packaging type | 9 |
also proposed | 9 |
see section | 9 |
large population | 9 |
empirical results | 9 |
west africa | 9 |
semantic similarity | 9 |
current situation | 9 |
experiment results | 9 |
component analysis | 9 |
commons attribution | 9 |
second one | 9 |
human proteome | 9 |
topological analysis | 9 |
third party | 9 |
isolation rate | 9 |
initial number | 9 |
scale networks | 9 |
bond configuration | 9 |
hantavirus research | 9 |
phylogenetic tree | 9 |
dimensionality reduction | 9 |
network management | 9 |
intergovernmental relations | 9 |
information theory | 9 |
economic development | 9 |
disease surveillance | 9 |
many studies | 9 |
largest connected | 9 |
first case | 9 |
email address | 9 |
clusters made | 9 |
zika virus | 9 |
first one | 9 |
law statistical | 9 |
exponential distribution | 9 |
com scientificreports | 9 |
dynamical correlations | 9 |
hierarchical clustering | 9 |
forward neural | 9 |
classification system | 9 |
network consists | 9 |
interaction among | 9 |
networks key | 9 |
fourier transform | 9 |
computational biology | 9 |
connected layer | 9 |
email model | 9 |
may well | 9 |
healthcare professionals | 9 |
cell lines | 9 |
behavioral responses | 9 |
wing parties | 9 |
data fusion | 9 |
psychomotor symptoms | 9 |
poisson distribution | 9 |
population structure | 9 |
will help | 9 |
distributions typical | 9 |
cargo capacity | 9 |
set enrichment | 9 |
infected samples | 9 |
entropy provides | 9 |
individuals will | 9 |
political debate | 9 |
partial differential | 9 |
ais data | 9 |
initial infection | 9 |
transmission rates | 9 |
pacific cargo | 9 |
venture capital | 9 |
world phenomenon | 9 |
directed validated | 9 |
higher number | 9 |
perinatal mood | 9 |
specific volume | 9 |
word vec | 9 |
based blockchain | 9 |
supply base | 9 |
daily counts | 9 |
mechanisms underlying | 9 |
omic data | 9 |
personal protection | 9 |
spreading ability | 9 |
partner agencies | 9 |
anxiety disorders | 9 |
minimum number | 9 |
research portfolios | 9 |
form sets | 9 |
public policy | 9 |
network nodes | 9 |
critical states | 9 |
public administration | 9 |
bond markets | 9 |
complex diseases | 9 |
evolutionary game | 9 |
natural history | 9 |
get infected | 9 |
wavelet coefficients | 9 |
structure information | 9 |
sequence similarity | 9 |
heterogeneous mixing | 9 |
two diffusion | 9 |
stochastic sirs | 9 |
edges connecting | 9 |
passenger traffic | 9 |
research institution | 9 |
time delay | 9 |
different aspects | 9 |
second half | 9 |
image quality | 9 |
node degrees | 9 |
word co | 9 |
develop new | 9 |
early stages | 9 |
epistemic community | 9 |
wicked problem | 9 |
will provide | 9 |
time varying | 9 |
quality control | 9 |
icu scenario | 9 |
strategies based | 9 |
strongly connected | 9 |
wt samples | 9 |
event period | 9 |
large network | 9 |
network layer | 9 |
infected population | 9 |
convolutional layer | 9 |
human immunodeficiency | 9 |
network epidemic | 9 |
tensor regression | 9 |
united nations | 9 |
sequence length | 9 |
network immunization | 9 |
ten years | 9 |
control samples | 9 |
debt crisis | 9 |
first three | 9 |
physical chemistry | 9 |
file system | 9 |
covid infected | 9 |
significantly reduce | 9 |
kernel function | 9 |
original data | 9 |
robot programming | 9 |
functional category | 9 |
significant impact | 9 |
inferring networks | 9 |
residual learning | 9 |
regular random | 9 |
final number | 9 |
pilot program | 9 |
triple helix | 9 |
average scimago | 9 |
one individual | 9 |
dual space | 9 |
blood vessels | 9 |
around sciacalli | 9 |
incubation periods | 9 |
ode model | 9 |
one year | 8 |
two individuals | 8 |
different sources | 8 |
universal study | 8 |
shot learning | 8 |
infected patients | 8 |
hours post | 8 |
stochastic sir | 8 |
incoming edges | 8 |
networks modeling | 8 |
large enough | 8 |
diffusive process | 8 |
launch stage | 8 |
hospitality management | 8 |
significant effect | 8 |
fsa business | 8 |
control chart | 8 |
remote connectivity | 8 |
data storage | 8 |
relations among | 8 |
team meetings | 8 |
data used | 8 |
obtained using | 8 |
two groups | 8 |
default diffusion | 8 |
indirectly connected | 8 |
prior knowledge | 8 |
first level | 8 |
also referred | 8 |
low values | 8 |
high number | 8 |
disease spreads | 8 |
high school | 8 |
six months | 8 |
estrogen receptor | 8 |
hidden neurons | 8 |
fixed costs | 8 |
centrally placed | 8 |
laplacian matrix | 8 |
tie formation | 8 |
case management | 8 |
epidemic curves | 8 |
national innovation | 8 |
regularized lstm | 8 |
new generation | 8 |
empirical networks | 8 |
main idea | 8 |
making process | 8 |
var threshold | 8 |
metropolitan area | 8 |
information transmission | 8 |
systematic literature | 8 |
predictive power | 8 |
susceptible neighbors | 8 |
follow links | 8 |
captcha recognition | 8 |
graphical games | 8 |
mining techniques | 8 |
time consuming | 8 |
patient records | 8 |
entrepreneur may | 8 |
study provides | 8 |
infected cases | 8 |
field approximation | 8 |
two weeks | 8 |
international journal | 8 |
web search | 8 |
diffusion rates | 8 |
generate new | 8 |
binding site | 8 |
much better | 8 |
microphone array | 8 |
methods used | 8 |
rewiring probability | 8 |
financial resources | 8 |
tailed distribution | 8 |
art methods | 8 |
world property | 8 |
numerical results | 8 |
research directions | 8 |
wearing facemasks | 8 |
symptom network | 8 |
brain network | 8 |
particularly interesting | 8 |
kco network | 8 |
networks tend | 8 |
network inferred | 8 |
cited paper | 8 |
confusion matrix | 8 |
first part | 8 |
virus disease | 8 |
significant reduction | 8 |
icu staff | 8 |
three centrality | 8 |
single node | 8 |
entirely agree | 8 |
proposed framework | 8 |
field equations | 8 |
binding pocket | 8 |
texture evolution | 8 |
diffusion equation | 8 |
depth analysis | 8 |
network features | 8 |
multiple affiliations | 8 |
several methods | 8 |
conditional probability | 8 |
robot poses | 8 |
network types | 8 |
mean correlation | 8 |
academic achievement | 8 |
area network | 8 |
new product | 8 |
behavior transmission | 8 |
structure affects | 8 |
verified user | 8 |
average value | 8 |
data type | 8 |
infected vertices | 8 |
higher levels | 8 |
mapreduce paradigm | 8 |
high risk | 8 |
induced tipping | 8 |
preserving machine | 8 |
worth mentioning | 8 |
molecular networks | 8 |
two authors | 8 |
service organizations | 8 |
motor skills | 8 |
rumor spreading | 8 |
using two | 8 |
will use | 8 |
performance evaluation | 8 |
transmission process | 8 |
spread among | 8 |
relative position | 8 |
across layers | 8 |
syndromic surveillance | 8 |
target interactions | 8 |
given emotion | 8 |
low latency | 8 |
results demonstrate | 8 |
see appendix | 8 |
articulated shape | 8 |
supporting file | 8 |
finite set | 8 |
network efficiency | 8 |
type ii | 8 |
homogeneous networks | 8 |
complete graph | 8 |
weak commutation | 8 |
maximum likelihood | 8 |
computational approaches | 8 |
permutation test | 8 |
aviation industry | 8 |
based method | 8 |
active compounds | 8 |
data may | 8 |
metabolic rate | 8 |
driven network | 8 |
randomly placed | 8 |
using genie | 8 |
high computational | 8 |
us consider | 8 |
ordinary differential | 8 |
network configuration | 8 |
illness universal | 8 |
fish stocks | 8 |
annealed networks | 8 |
given network | 8 |
stochastic block | 8 |
node networks | 8 |
emotional contagion | 8 |
important nodes | 8 |
also allows | 8 |
nodes based | 8 |
real systems | 8 |
sky blue | 8 |
mouth disease | 8 |
acoustic signal | 8 |
findings suggest | 8 |
disparity estimation | 8 |
microarray data | 8 |
model using | 8 |
stochastic gradient | 8 |
scale separation | 8 |
networks around | 8 |
literature mining | 8 |
physical health | 8 |
noise ratio | 8 |
four different | 8 |
based self | 8 |
shallow water | 8 |
many others | 8 |
computational cost | 8 |
make sense | 8 |
underlying social | 8 |
structural changes | 8 |
second step | 8 |
correlation matrix | 8 |
health problems | 8 |
anxiety symptoms | 8 |
much higher | 8 |
smart grids | 8 |
independent variable | 8 |
gold standard | 8 |
dynamic process | 8 |
new nodes | 8 |
determined using | 8 |
intervention strategies | 8 |
biomedical networks | 8 |
functional form | 8 |
false paths | 8 |
manuscript submitted | 8 |
stochastic processes | 8 |
immune network | 8 |
interaction map | 8 |
higher quality | 8 |
carrier protein | 8 |
maximum degree | 8 |
new ideas | 8 |
service delivery | 8 |
sector i | 8 |
will see | 8 |
used data | 8 |
infection rates | 8 |
approach using | 8 |
important issue | 8 |
neighbor network | 8 |
next generation | 8 |
expected shortfall | 8 |
reconstructed networks | 8 |
global scale | 8 |
data acquisition | 8 |
given protein | 8 |
mycobacterium avium | 8 |
quality images | 8 |
disease propagation | 8 |
widely accepted | 8 |
si model | 8 |
offline networks | 8 |
mental disorders | 8 |
th time | 8 |
new products | 8 |
organized criticality | 8 |
echo chambers | 8 |
cluster together | 8 |
aerial vehicles | 8 |
data technologies | 8 |
students tend | 8 |
close contact | 8 |
uav detection | 8 |
average distance | 8 |
become increasingly | 8 |
walk engine | 8 |
key factor | 8 |
authors showed | 8 |
sequence data | 8 |
jaccard similarity | 8 |
voter models | 8 |
disruption risks | 8 |
stable equilibrium | 8 |
exclusively present | 8 |
full freighters | 8 |
two aspects | 8 |
spread across | 8 |
relevant nodes | 8 |
host pathways | 8 |
short average | 8 |
depressive symptoms | 8 |
percolation theory | 8 |
research suggests | 8 |
network risk | 8 |
two proteins | 8 |
epidemic prevention | 8 |
fourth block | 8 |
carlo method | 8 |
transition point | 8 |
specific disease | 8 |
computing power | 8 |
marine biodiversity | 8 |
protein targets | 8 |
new business | 8 |
radial roads | 8 |
energy function | 8 |
physical systems | 8 |
network effectiveness | 8 |
global public | 8 |
social internet | 8 |
temporal dimension | 8 |
recent study | 8 |
protein docking | 8 |
scimago rank | 8 |
type specific | 8 |
three cases | 8 |
collaboration network | 8 |
region based | 8 |
immune responses | 8 |
semantic segmentation | 8 |
public domain | 8 |
chain risk | 8 |
clustering coefficients | 8 |
growth factor | 8 |
network shows | 8 |
available information | 8 |
user experiences | 8 |
network coupled | 8 |
network formation | 8 |
study group | 8 |
cerebral cortex | 8 |
expected degrees | 8 |
based solutions | 8 |
networks represent | 8 |
just one | 8 |
entire population | 8 |
first author | 8 |
network approaches | 8 |
one needs | 8 |
related discovery | 8 |
spectral clustering | 8 |
infection events | 8 |
resource allocation | 8 |
make sure | 8 |
large size | 8 |
emergency response | 8 |
sovereign debt | 8 |
confined geographically | 8 |
april th | 8 |
adversarial robustness | 8 |
antiviral drugs | 8 |
one example | 8 |
becoming infected | 8 |
spillover network | 8 |
given node | 8 |
association networks | 8 |
making use | 8 |
face recognition | 8 |
assortative mixing | 8 |
graph distance | 8 |
following section | 8 |
per day | 8 |
characteristic time | 8 |
size increases | 8 |
several reasons | 8 |
information management | 8 |
positive relationship | 8 |
nearest degree | 8 |
semantic similarities | 8 |
ising model | 8 |
systemic risks | 8 |
interlayer edges | 8 |
using gene | 8 |
perform better | 8 |
temporal evolution | 8 |
feed forward | 8 |
transmitted diseases | 8 |
theoretical analysis | 8 |
dissipative systems | 8 |
market orientation | 8 |
individual behavior | 8 |
real interpersonal | 8 |
cellular systems | 8 |
relay candidates | 8 |
quality measures | 8 |
statistical significance | 8 |
weight capacity | 8 |
er graph | 8 |
go beyond | 8 |
networks via | 8 |
positively correlated | 8 |
north america | 8 |
diversified finance | 8 |
collected data | 8 |
dataset contains | 8 |
without loss | 8 |
existing studies | 8 |
including sars | 8 |
using social | 8 |
frequently used | 8 |
generally speaking | 8 |
early stage | 8 |
misinformation outbreak | 8 |
systematic analysis | 8 |
correlations among | 8 |
using np | 8 |
close proximity | 8 |
unmanned aerial | 8 |
available online | 8 |
clustered network | 8 |
hidden neuron | 8 |
interests include | 8 |
main steps | 8 |
given degree | 8 |
one important | 8 |
earth system | 7 |
state nodes | 7 |
theoretical approaches | 7 |
future relationships | 7 |
computer viruses | 7 |
test persons | 7 |
layered networks | 7 |
allowed us | 7 |
fitness function | 7 |
driven model | 7 |
non trivial | 7 |
care medicine | 7 |
different time | 7 |
attack tolerance | 7 |
model training | 7 |
three objective | 7 |
significantly smaller | 7 |
sr networks | 7 |
gas consumption | 7 |
basic features | 7 |
also help | 7 |
research field | 7 |
disease clustering | 7 |
data size | 7 |
regression methods | 7 |
new approaches | 7 |
also revealed | 7 |
object recognition | 7 |
hole spanner | 7 |
associated host | 7 |
fixed wing | 7 |
community bridges | 7 |
microsoft hololens | 7 |
level representation | 7 |
structural descriptors | 7 |
least two | 7 |
similar way | 7 |
studied using | 7 |
threshold models | 7 |
offline network | 7 |
predicted network | 7 |
adjacency matrices | 7 |
final network | 7 |
epidemic starting | 7 |
level factors | 7 |
particular interest | 7 |
epidemic control | 7 |
indirect flights | 7 |
associated proteins | 7 |
earlier times | 7 |
turn behavior | 7 |
study showed | 7 |
inference method | 7 |
changing circumstances | 7 |
low levels | 7 |
jak stat | 7 |
quantitative analysis | 7 |
diffusion model | 7 |
second part | 7 |
sectoral tail | 7 |
every individual | 7 |