quadgram

This is a table of type quadgram and their frequencies. Use it to search & browse the list to learn more about your study carrel.

quadgram	frequency
q q q q	236
on the other hand	109
the size of the	89
the total number of	86
is the number of	79
in the context of	79
in the case of	78
can be used to	71
as well as the	60
a large number of	59
severe acute respiratory syndrome	52
the structure of the	51
the rest of the	51
one of the most	50
at the same time	50
it is important to	49
the number of infected	43
is one of the	43
the number of nodes	41
the performance of the	41
as the number of	40
it is possible to	40
is based on the	40
the value of the	39
as a function of	38
in the form of	38
as a result of	38
in terms of the	37
the evolution of the	37
with respect to the	37
in the human brain	37
the results of the	36
and the time of	35
article is protected by	34
this article is protected	34
is protected by copyright	34
the time of exposure	34
the units in the	34
on the number of	34
of the number of	33
as shown in fig	32
to the number of	32
a small number of	32
wearing condition identification network	31
the beginning of the	31
nodes in the network	31
can be used for	31
forward and backward disruption	31
the analysis of the	30
the average number of	30
the development of the	29
it is necessary to	28
in the number of	28
the quality of the	28
for the analysis of	28
in the field of	28
on the basis of	27
are more likely to	27
spread of infectious diseases	27
the end of the	27
are shown in figure	26
the same number of	26
the fact that the	26
a wide range of	26
as shown in figure	26
from the perspective of	26
it can be seen	25
number of infected nodes	25
at the beginning of	25
and backward disruption propagation	25
is organized as follows	25
the complex network theory	25
is shown in figure	25
in a number of	25
the probability that a	24
can be found in	24
is the total number	24
the spread of infectious	24
the distribution of the	24
for each of the	24
the average degree of	24
of the network is	23
and the number of	23
the nodes in the	23
structure of the network	23
cases at d p	23
is due to the	23
in addition to the	23
in the face of	23
of the network structure	23
the spread of disease	22
the basic reproduction number	22
is stored in the	22
the data from the	22
at the end of	22
paper is organized as	21
neural networks in the	21
on the one hand	21
proteins targeted by sars	21
one of the main	21
the information content of	21
in the presence of	21
of the network and	21
number of infected individuals	21
model to study the	21
the number of edges	21
the relationship between the	20
the spread of the	20
is defined as the	20
networks with community structure	20
the number of links	20
due to the fact	20
to be able to	20
machine learning techniques in	20
at each time step	20
to the fact that	20
of the sr network	19
such as the zipf	19
a function of the	19
an important role in	19
is shown in fig	19
that can be used	19
more likely to be	19
during the training process	19
there is a big	19
the role of social	19
the input data and	19
we find that the	19
can be seen in	19
neural network model to	19
experimental data about the	19
the largest eigenvalue of	19
of severe acute respiratory	19
a number of important	18
to the ability to	18
on the data from	18
is consistent with the	18
based on the data	18
the data about the	18
with the help of	18
one or more hidden	18
of the epidemic threshold	18
of the road network	18
a neural network has	18
in order to predict	18
by a factor of	18
and aboav scaling laws	18
or more hidden layers	18
a big amount of	18
in the study of	18
can be defined as	18
the middle of the	18
while there is a	18
by the number of	18
the accuracy of the	18
has been shown to	18
as can be seen	18
are shown in table	18
in the output layer	18
is a big amount	17
the network representation for	17
results in the scale	17
optimized design of novel	17
their activations based on	17
the material composition and	17
adhesion is called tribology	17
transfer function and transmit	17
data from the input	17
a more applied branch	17
computer models somewhat resembling	17
such as the granular	17
network more accurate in	17
studies is that while	17
typical ann model has	17
transmit it to the	17
units with varying connection	17
to apply machine learning	17
a series of functions	17
activations based on the	17
linear transfer function and	17
units in the hidden	17
and engineers often deal	17
novel hydrophobic and superhydrophobic	17
weights of the inter	17
it has been recently	17
model has interconnected nodes	17
the synaptic weights of	17
conditions of the experiment	17
colloidal crystals made of	17
one of the challenges	17
correlations allows predicting water	17
by examining individual records	17
crystals made of small	17
parameters are determined in	17
and surface roughness or	17
in the synaptic weights	17
they are suited for	17
stored in the synaptic	17
challenges in the tribological	17
friction and the wear	17
units in the output	17
tribology remains a data	17
of exposure to liquid	17
the ability to represent	17
important scaling relationships such	17
is that while there	17
determined in an experimental	17
properties of metallic composite	17
made of small rigid	17
by examples and training	17
connection weights until the	17
the tribological studies is	17
predict surface wetting properties	17
closely related to colloidal	17
including complex dependencies between	17
in the process of	17
from the input layer	17
the wetting properties of	17
compute their activations based	17
and adhesion is called	17
conclude that using the	17
that while there is	17
graphite composite including complex	17
it to the output	17
networks in the human	17
neurons and synapses in	17
ability to represent complex	17
behavior and in a	17
layer compute their activations	17
to process the input	17
results are finally delivered	17
data about the material	17
suggested to apply machine	17
with units representing the	17
accurate in predicting new	17
with varying connection weights	17
input data and convert	17
representing the input data	17
and levitating droplet clusters	17
hydrophobic and superhydrophobic materials	17
ann model has interconnected	17
used in wetting experiments	17
as the coefficient of	17
to predict surface wetting	17
process is stored in	17
for the spread of	17
colloidal science is surface	17
network has three parts	17
related to colloidal science	17
finally delivered to the	17
the optimized design of	17
of metallic composite materials	17
knowledge acquired during the	17
of surfaces such as	17
of functions to process	17
of a ductile iron	17
scaling relationships such as	17
deals with such characteristics	17
ann models learn by	17
contacting surfaces as the	17
delivered to the units	17
somewhat resembling neural networks	17
more accurate in predicting	17
of experimental data about	17
model complex neurons and	17
the center of the	17
until the results are	17
with parameters of surfaces	17
repellent properties of metallic	17
to study the wetting	17
the input layer with	17
are computer models somewhat	17
big amount of experimental	17
prediction for each record	17
the results are finally	17
incorporate a series of	17
learn by examples and	17
to the units in	17
in predicting new outcomes	17
functions to process the	17
using the network representation	17
into the desired output	17
properties of various materials	17
surface scientists and engineers	17
apply machine learning techniques	17
time of exposure to	17
the challenges in the	17
recently suggested to apply	17
we conclude that using	17
surfaces as the coefficient	17
these correlations allows predicting	17
synaptic weights of the	17
and the wear rate	17
parameters of surfaces such	17
network model to study	17
the knowledge acquired during	17
neural network has three	17
of friction and the	17
more applied branch of	17
and surface properties of	17
on the spread of	17
since ann models learn	17
characteristics of contacting surfaces	17
a typical ann model	17
are determined in an	17
series of functions to	17
properties of a ductile	17
science is surface science	17
composition and surface roughness	17
or conditions of the	17
data about the frictional	17
the first physical principles	17
multilayer perception neural network	17
synapses in the human	17
and retrieving acquired knowledge	17
weights until the results	17
number of important scaling	17
anns learn by examining	17
the training process is	17
applied a multilayer perception	17
hidden layer compute their	17
representation for colloidal systems	17
connections leading to the	17
of important scaling relationships	17
storing and retrieving acquired	17
generating the prediction for	17
metallic composite materials for	17
connect the units with	17
of the giant component	17
several stages into the	17
for the optimized design	17
roughness or conditions of	17
tribological studies is that	17
to represent complex input	17
the droplets used in	17
and transmit it to	17
training makes the network	17
hidden layers connect the	17
as the granular material	17
the nature of the	17
between the contact angle	17
material composition and surface	17
interconnected nodes that model	17
anns incorporate a series	17
of the eighteenth century	17
of the droplets used	17
dependencies between the contact	17
convert them over several	17
and convert them over	17
and in a number	17
it is worth noting	17
they cannot be predicted	17
three parts or layers	17
deal with parameters of	17
learn by examining individual	17
learning techniques in order	17
the units with varying	17
leading to the ability	17
result with the prediction	17
engineers often deal with	17
such as surface roughness	17
has interconnected nodes that	17
as the data about	17
resembling neural networks in	17
be predicted from the	17
about the material composition	17
can be applied to	17
has been recently suggested	17
nodes that model complex	17
for the purpose of	17
stages into the desired	17
materials for the optimized	17
the hidden layer compute	17
with such characteristics of	17
for storing and retrieving	17
more hidden layers connect	17
has three parts or	17
another area closely related	17
the impact of the	17
of contacting surfaces as	17
complex neurons and synapses	17
of small rigid particles	17
function and transmit it	17
a multilayer perception neural	17
suited for storing and	17
which deals with friction	17
order to predict surface	17
interdisciplinary area is highly	17
surface roughness or conditions	17
acquired during the training	17
surface properties of various	17
network representation for colloidal	17
models somewhat resembling neural	17
these parameters are determined	17
composite including complex dependencies	17
composite materials for the	17
such as the data	17
to the output layer	17
relationships such as the	17
training process is stored	17
coefficient of friction and	17
that using the network	17
size of the droplets	17
units representing the input	17
we are interested in	17
nodal connections leading to	17
the coefficient of friction	17
wetting properties of a	17
the result with the	17
perception neural network model	17
tribology deals with such	17
of the challenges in	17
models learn by examples	17
makes the network more	17
such characteristics of contacting	17
surfaces such as surface	17
been recently suggested to	17
droplets used in wetting	17
predicted from the first	17
scientists and engineers often	17
input layer and a	17
them over several stages	17
over several stages into	17
layers connect the units	17
complex dependencies between the	17
cannot be predicted from	17
the result of the	17
in the tribological studies	17
applied branch of surface	17
this interdisciplinary area is	17
and water contact angle	17
the network more accurate	17
are finally delivered to	17
that model complex neurons	17
area closely related to	17
and they cannot be	17
are suited for storing	17
layer and a non	17
amount of experimental data	17
to colloidal science is	17
input layer with units	17
often deal with parameters	17
comparing the result with	17
design of novel hydrophobic	17
study the wetting properties	17
in an experimental manner	17
the prediction for each	17
the input layer and	17
layer with units representing	17
area is highly empirical	17
process the input data	17
understanding these correlations allows	17
free behavior and in	17
varying connection weights until	17
from the first physical	17
in the hidden layer	17
the effect of the	17
branch of surface science	17
and synapses in the	17
as shown in table	17
data and convert them	17
techniques in order to	17
of novel hydrophobic and	17
the robustness of the	16
results show that the	16
the dynamics of the	16
the proposed or protocol	16
at the level of	16
a measure of the	16
n is the total	16
with the number of	16
the number of individuals	16
rest of the paper	16
ties with a frequency	16
of the neural network	16
be used for the	16
in this section we	16
a consequence of the	16
electrical conducting polymer nanocomposite	16
the internet of things	16
in the united states	16
state of the art	16
in the area of	16
average degree of the	15
the behavior of the	15
outbreak and after peak	15
the importance of the	15
interact with each other	15
the network structure and	15
is characterized by the	15
the spread of a	15
that there is a	15
to the best of	15
the probability of a	15
the target data stream	15
that the number of	15
the mathematical theory of	15
of this paper is	15
in relation to the	15
shown in figure a	15
in wireless sensor networks	15
the majority of the	15
and n is the	15
can be used as	15
of the adjacency matrix	15
the rate at which	15
the state of the	15
is given by where	15
a better understanding of	15
in the human interactome	14
can be seen that	14
the output of the	14
in old at cells	14
the expected number of	14
a key role in	14
number of susceptible nodes	14
the case of the	14
the best of our	14
the default mode network	14
is related to the	14
we found that the	14
fraction of the population	14
the understanding of the	14
size of the giant	14
in order to improve	14
in the absence of	14
best of our knowledge	14
taking into account the	14
the paper is organized	14
is given by the	14
nodes i and j	14
where n is the	14
the sum of the	14
it should be noted	14
the number of susceptible	14
has the potential to	13
a convolutional neural network	13
the context of the	13
the after peak stage	13
the use of the	13
l p r e	13
in the private sphere	13
the final deconvolutional layer	13
the effects of the	13
a l p r	13
in detail in the	13
a result of the	13
o u r n	13
the world health organization	13
r n a l	13
scaling in random networks	13
the values of the	13
the difference between the	13
emergence of scaling in	13
the role of the	13
as a consequence of	13
to the set of	13
the topology of the	13
in the limit of	13
of the nodes in	13
is determined by the	13
is worth noting that	13
p r o o	13
of the paper is	13
j o u r	13
when it comes to	13
in the previous section	13
of an innovation system	13
individuals in the population	13
r o o f	13
n a l p	13
at the time of	13
is proportional to the	13
this means that the	13
of scaling in random	13
middle east respiratory syndrome	13
u r n a	13
can also be used	13
is a measure of	13
the construction of the	12
dynamics and control of	12
the edges of the	12
i is the number	12
is shown in table	12
have been used to	12
east respiratory syndrome coronavirus	12
an example of a	12
to the development of	12
network structure and the	12
for disease control and	12
community structure in networks	12
the extent to which	12
be explained by the	12
the degree of the	12
development of the network	12
to the spread of	12
number of nodes in	12
disease control and prevention	12
of nodes in the	12
can be seen from	12
results are shown in	12
can be represented as	12
in the thermodynamic limit	12
important role in the	12
we are able to	12
reuse allowed without permission	12
the time to detection	12
is important to note	12
based on the number	12
it is difficult to	12
right and right wing	12
the case of a	12
an overview of the	12
been shown to be	12
on the relationship between	12
the length of the	12
with the increase of	12
that most of the	12
no reuse allowed without	12
between innovation and financing	12
we observe that the	12
of social media on	12
is based on a	12
in the sense that	12
in the middle of	12
it is found that	12
improve the performance of	12
node in the network	12
as illustrated in fig	12
we see that the	12
of electrical conducting polymer	12
is a set of	12
structure and function of	12
by the complex network	12
degree of the network	12
in the southern ocean	12
during the after peak	11
license to display the	11
display the preprint in	11
be discussed more in	11
the description of the	11
of the most popular	11
a license to display	11
in the next section	11
be used as a	11
social media use and	11
of nodes and edges	11
for the sake of	11
medrxiv a license to	11
nodes in a network	11
the stability of the	11
can be estimated using	11
spread of the disease	11
in each of the	11
as well as a	11
discussed more in detail	11
the average path length	11
the large number of	11
is characterized by a	11
are shown in fig	11
it has been shown	11
using the data from	11
the port of antwerp	11
who has granted medrxiv	11
which is consistent with	11
relationship between eo and	11
is referred to as	11
are likely to be	11
as part of the	11
networking in the private	11
coupling between innovation and	11
is the author funder	11
x for peer review	11
the fraction of nodes	11
more in detail in	11
of the social network	11
which is given by	11
for peer review of	11
this is due to	11
is the probability that	11
not certified by peer	11
if the number of	11
the form of a	11
as a proxy for	11
was not certified by	11
there has been a	11
and its impact on	11
granted medrxiv a license	11
should be noted that	11
the vast majority of	11
by the shannon entropy	11
which indicates that the	11
certified by peer review	11
will be discussed more	11
the neural network model	11
is defined by the	11
in order to understand	11
acute respiratory syndrome coronavirus	11
used in materials science	11
the maximum number of	11
the preprint in perpetuity	11
in networks with community	11
in the public sphere	11
the number of interactions	11
that is to say	11
can be seen as	11
to a large extent	11
to display the preprint	11
also be used to	11
the epidemic threshold is	11
the lead apa network	11
has granted medrxiv a	11
and the spread of	11
is the sum of	11
in such a way	11
the meta path detection	10
is the fraction of	10
the structure and function	10
the ratio of the	10
exponent in the curve	10
the shannon entropy is	10
of the contact network	10
we would like to	10
fitted curve is hyperbolic	10
number of nodes and	10
following values were obtained	10
the following values were	10
also used for various	10
average shortest path length	10
for a long time	10
results showed that the	10
a crucial role in	10
curve fitting equation is	10
wetting transitions and stick	10
given by its profile	10
of the network in	10
informational content in the	10
the existence of a	10
the degree distribution of	10
various other aspects of	10
state and n is	10
discussion of d colloidal	10
with the same number	10
in a way that	10
on the analysis of	10
equation is almost one	10
the fitted curve is	10
at any given time	10
a surface roughness parameter	10
surface given by its	10
total number of states	10
spread of a disease	10
the reliability of the	10
content in the surface	10
characterizing informational content in	10
the choice of the	10
is considered to be	10
as a surface roughness	10
is the degree of	10
important to note that	10
of deep neural networks	10
to the study of	10
see also a discussion	10
see materials and methods	10
the antwerp port authority	10
that the fitted curve	10
can be divided into	10
figure and tables and	10
n is the number	10
is used in materials	10
parameter characterizing informational content	10
in the surface given	10
of the power exponent	10
with higher pagerank in	10
a higher level of	10
the input to the	10
for the development of	10
between financing and innovation	10
of d colloidal clusters	10
value of the power	10
the data from figure	10
the curve fitting equation	10
than the number of	10
for various other aspects	10
indicates that the fitted	10
in order to identify	10
the global spread of	10
of a distribution is	10
can be written as	10
as wetting transitions and	10
a set of nodes	10
characterized by the shannon	10
used for various other	10
is a consequence of	10
centers for disease control	10
in the network of	10
is the set of	10
the peak of the	10
by janai et al	10
the complexity of the	10
in the curve fitting	10
science can be used	10
clusters by janai et	10
used for the analysis	10
surface roughness parameter characterizing	10
power exponent in the	10
from figure and tables	10
such as wetting transitions	10
an estimation of the	10
in the near future	10
a discussion of d	10
other aspects of surface	10
roughness parameter characterizing informational	10
the small world network	10
a distribution is characterized	10
the ppi network of	10
the power exponent in	10
probability of the n	10
in a network with	10
the network can be	10
the statistical probability of	10
scn a sodium voltage	10
processes in complex networks	10
data from figure and	10
and can be used	10
information content of a	10
distribution is characterized by	10
in contrast to the	10
th state and n	10
the presence of a	10
in most of the	10
is used as a	10
informational approach is also	10
entropy is used in	10
spread of epidemic disease	10
and after peak stages	10
structure of the contact	10
the surface given by	10
the number of bits	10
is also used for	10
the remainder of the	10
epidemic spreading in scale	10
on the dynamics of	10
also a discussion of	10
the number of neighbors	10
aspects of surface science	10
approach is also used	10
colloidal clusters by janai	10
statistical probability of the	10
fitting equation is almost	10
nodes of the network	10
it is clear that	10
can be considered as	10
d colloidal clusters by	10
network science can be	10
somewhat similar to the	10
have shown that the	10
that the structure of	10
our goal is to	10
content of a distribution	10
have been proposed to	10
shannon entropy is used	10
based informational approach is	10
of the network of	10
is the statistical probability	10
is more random than	9
the targeted immunization strategy	9
the impact of social	9
methods of network science	9
for internet of things	9
one could expect that	9
estimation of the information	9
compared to the other	9
epidemic disease on networks	9
and characterized by power	9
the topological structure of	9
infectious diseases in humans	9
is also characteristic for	9
the raw movement data	9
systems form sets of	9
a description of the	9
in order to evaluate	9
has been shown that	9
be considered as a	9
total number of susceptible	9
this is because the	9
it is essential to	9
the probability that the	9
of social media use	9
defined as the number	9
the value of s	9
in the following chapter	9
of the total population	9
human proteins targeted by	9
based on the analysis	9
in the social sciences	9
the crisis management team	9
final number of infected	9
using the shannon entropy	9
epidemic processes in complex	9
a comparative study of	9
a high degree of	9
these structures can be	9
on the structure of	9
similar to each other	9
the analysis of various	9
in response to the	9
networks epidemic spreading in	9
of the shannon entropy	9
of the community structure	9
the number of contacts	9
ks p i q	9
which is defined as	9
distributions typical for the	9
detail in the following	9
of gene ly e	9
of epidemic disease on	9
which means that the	9
the number of layers	9
expressed outside the lungs	9
the power law distribution	9
random than the eight	9
is a function of	9
content in these configurations	9
the shannon entropy provides	9
given by where pn	9
similar to the set	9
evaluate the performance of	9
can be regarded as	9
typical for the latter	9
the properties of the	9
and clusters made of	9
of configurations somewhat similar	9
physical chemistry and materials	9
in the network is	9
the application of the	9
the neural network regime	9
the same functional category	9
the definition of the	9
made of small particles	9
bond cluster is more	9
small particles or droplets	9
friendship and study assistance	9
the final number of	9
degree distribution of the	9
of the entrepreneurial process	9
the number of connections	9
the vertex entity mask	9
the relative importance of	9
and function of complex	9
a large social network	9
distribution is also characteristic	9
of small particles or	9
value of the shannon	9
configurations somewhat similar to	9
cluster is more random	9
by means of a	9
of the nineteenth century	9
the use of a	9
for the study of	9
can be represented by	9
the social discourse around	9
various systems studied by	9
as well as to	9
for wireless sensor networks	9
such systems form sets	9
many such systems form	9
pn is the statistical	9
information content in these	9
size of the brain	9
be found in the	9
these include granular materials	9
total number of infected	9
in order to obtain	9
in the spread of	9
expect that the seven	9
in the immune response	9
than or equal to	9
where pn is the	9
have been developed to	9
is likely to be	9
the performance of our	9
journal of transport geography	9
for the detection of	9
statistical mechanics of complex	9
by where pn is	9
would like to thank	9
the information content in	9
studies have shown that	9
edges in the network	9
the shape of the	9
structures can be estimated	9
of the information content	9
with strong community structure	9
same number of nodes	9
in terms of their	9
on the impact of	9
in the network and	9
content of these structures	9
the health status of	9
provides an estimation of	9
with the same degree	9
of the stochastic system	9
of these structures can	9
entropy provides an estimation	9
clusters made of small	9
by physical chemistry and	9
the shannon entropy s	9
basic reproduction number r	9
the spread of information	9
be estimated using the	9
systems studied by physical	9
the results show that	9
in the sis model	9
the tail risk network	9
the shannon entropy approach	9
studied by physical chemistry	9
it is assumed that	9
power law distribution is	9
each node in the	9
the supply chain network	9
to that of the	9
to conclude this section	9
statistical distributions typical for	9
more random than the	9
sets of configurations somewhat	9
as long as the	9
shannon entropy provides an	9
of network science can	9
the set of symbols	9
of various systems studied	9
the community structure in	9
role of social media	9
number of shortest paths	9
law statistical distributions typical	9
the critical value of	9
facial detection and cropping	9
the number of samples	9
estimated using the shannon	9
play an important role	9
could expect that the	9
have been used for	9
for the sis model	9
chemistry and materials science	9
form sets of configurations	9
of the degree distribution	9
of the total number	9
law distribution is also	9
which will be discussed	9
the immune response to	9
beginning of the eighteenth	9
information content of these	9
expressed in the lungs	9
analysis of various systems	9
the war of succession	9
while for the eight	9
as the fraction of	9
data in order to	9
equal to the number	9
function of complex networks	9
to deal with the	8
of infectious diseases and	8
on the development of	8
was based on the	8
be used to identify	8
was supported by the	8
infection and respiratory illness	8
process on a network	8
we assume that the	8
it is also important	8
for a single image	8
as compared to the	8
the study of the	8
general facial recognition model	8
to evaluate the performance	8
the results for the	8
may not always be	8
of distinct social networks	8
as well as in	8
as one of the	8
to the size of	8
the network and the	8
for the understanding of	8
media use and entrepreneurial	8
the impact of network	8
in order to generate	8
of forward and backward	8
perinatal mood and anxiety	8
the speed of the	8
in the real world	8
the cognitive distance between	8
of the most important	8
in this work we	8
networks with the same	8
order to improve the	8
characteristic for scalefree networks	8
by the fact that	8
it was found that	8
if there is a	8
of infected nodes in	8
the density of infected	8
could be used to	8
for the neural network	8
depends on the number	8
mathematical theory of epidemics	8
as a measure of	8
in a large social	8
be seen in the	8
the changes in the	8
the official accounts of	8
international journal of hospitality	8
in academic performance diffusion	8
are characterized by a	8
increase in the number	8
and respiratory illness universal	8
network coupled with its	8
has been used to	8
proteins in the lungs	8
with a small number	8
the network structure of	8
of the evolution of	8
from the same hospital	8
in this paper we	8
also referred to as	8
it is interesting to	8
can be interpreted as	8
makes it possible to	8
as well as by	8
of a collection of	8
in line with the	8
role of distinct social	8
the shortest path between	8
networks in academic performance	8
is also important to	8
can be derived from	8
not included in the	8
the authors declare no	8
respiratory illness universal study	8
with regard to the	8
influential spreaders in complex	8
has been applied to	8
the fraction of infected	8
also characteristic for scalefree	8
size of the network	8
in a social network	8
the rough net definition	8
the training set and	8
of initial infected nodes	8
based on the assumption	8
in covid infected lung	8
impact of social media	8
of the spanish road	8
it has to be	8
is greater than the	8
the validated network of	8
the spread of covid	8
there are a few	8
a high level of	8
is equal to the	8
distinct social networks in	8
presented in this paper	8
of a set of	8
was selected as the	8
to an increase in	8
the probability of an	8
one of the major	8
the members of the	8
in the management of	8
the implementation of the	8
a node in the	8
for each of these	8
topological structure of the	8
of anxiety and depression	8
of the same type	8
we show that the	8
genes with higher pagerank	8
as a basis for	8
some of the most	8
that social media use	8
to the emergence of	8
no conflict of interest	8
it is crucial to	8
social internet of things	8
of the network are	8
the role of distinct	8
that needs to be	8
the node with the	8
of a number of	8
mechanism of action of	8
the adjacency matrix a	8
the function of the	8
validated network of verified	8
is similar to the	8
performance of the proposed	8
a diverse set of	8
in the main text	8
use and entrepreneurial entry	8
on the assumption that	8
as the result of	8
of edges in the	8
be interpreted as a	8
to ensure that the	8
the outcome of the	8
be taken into account	8
to the extent that	8
of the sis model	8
of the program year	8
in a network of	8
a wide variety of	8
short average path length	8
structure and dynamics of	8
journal of hospitality management	8
is the case of	8
it can also be	8
that it is not	8
in online social networks	8
to the mathematical theory	8
social networks in academic	8
in the range of	8
can be achieved by	8
our results show that	8
the strength of the	8
be in one of	8
mechanics of complex networks	8
is the probability of	8
real interpersonal social network	8
for the number of	8
the authors found that	8
for different values of	8
paper is structured as	8
is structured as follows	8
manuscript submitted to acm	8
number of nodes that	8
tensorflow object detection api	8
at the expense of	8
viral infection and respiratory	8
the efficiency of the	8
methods have been developed	8
plays an important role	8
the duration of the	8
in this paper is	8
in the treatment condition	8
the magnitude of the	8
the next time step	8
is different from the	8
between i and j	8
between social media use	7
the sr network was	7
in the same functional	7
be seen from the	7
as shown in the	7
in a similar way	7
a large amount of	7
on the size of	7
a finite set of	7
consumption in the network	7
the epidemic threshold in	7
is smaller than the	7
the topological properties of	7
approaches have been proposed	7
of the system and	7
which is based on	7
studies have focused on	7
of the sensor network	7
that need to be	7
the epidemic threshold for	7
society of critical care	7
it is based on	7
proteins mainly expressed in	7
in the evolution of	7
the solution of the	7
is supported by the	7
homophily in social networks	7
a subset of the	7
in the test set	7
a power law degree	7
the network was trained	7
the increase of the	7
the network as a	7
a network can be	7
during the period of	7
the number of the	7
a set of influential	7
with the rest of	7
and the development of	7
referred to as the	7
are displayed in black	7
number of individuals in	7
coupling between financing and	7
of the distribution of	7
presence or absence of	7
this is consistent with	7
between nodes i and	7
spreaders in complex networks	7
such as the number	7
the human interactome network	7
play a key role	7
in the transmission of	7
the perspective of statistical	7
of the th century	7
of the network topology	7
of these networks is	7
this version posted may	7
which this version posted	7
an increase in the	7
this paper is organized	7
temporal exponential random graph	7
a kernel size of	7
it is also possible	7
to the case of	7
regulated in covid infected	7
in a population with	7
in the last decade	7
the degree of node	7
reach a steady state	7
for the final deconvolutional	7
the spreading of the	7
the interactions between the	7
network of verified users	7
in the supplementary material	7
raw movement data of	7
have been widely used	7
the newly paved roads	7
universal screening and referral	7
the spanish road network	7
which is a property	7
this is the first	7
in order to achieve	7
community structure in the	7
in a variety of	7
a member of the	7
the way in which	7
discovery network viral infection	7
of science and technology	7
the loan guarantee network	7
the presence of demographic	7
our findings indicate that	7
represents the number of	7
probability that a node	7
the number of doses	7
is often the case	7
critical value of the	7
of a node in	7
the different types of	7
calculated and subtracted out	7
for the case of	7
mainly expressed in the	7
social network size in	7
it was shown that	7
in the rest of	7
a network with a	7
the fsa business model	7
number of edges in	7
coupled with its snn	7
of this study is	7
the largest connected component	7
the second and third	7
of public research institutions	7
improve the accuracy of	7
of the immune system	7
a useful tool for	7
susceptible and infective individuals	7
the severe acute respiratory	7
edges of the network	7
both in terms of	7
is found that the	7
energy consumption in the	7
such a way that	7
metapopulation network coupled with	7
dynamics on complex networks	7
the fact that a	7
to be used in	7
a significant impact on	7
the basic reproductive number	7
which corresponds to the	7
and attack tolerance of	7
the location of the	7
is the adjacency matrix	7
with the same probability	7
the core of the	7
nodes of the same	7
better understanding of the	7
exponential random graph models	7
the purpose of this	7
the results showed that	7
the number of times	7
and the evolution of	7
that the epidemic threshold	7
largest eigenvalue of the	7
of the development of	7
network viral infection and	7
can be in one	7
the effects of different	7
of quantized neural networks	7
to address this problem	7
in the light of	7
in order to have	7
for the first time	7
a systematic literature review	7
the outbreak of the	7
the authors showed that	7
dynamical process on a	7
a review of the	7
are listed in table	7
the training of the	7
t is the number	7
in the ashwagandha network	7
in terms of time	7
time for a single	7
make use of the	7
will focus on the	7
it is possible that	7
the course of the	7
as described in section	7
connected to each other	7
without loss of generality	7
the performance of a	7
and the effect of	7
of dynamical processes on	7
the spread of diseases	7
of diseases in networks	7
growth and financial return	7
of critical care medicine	7
total number of nodes	7
the validity of the	7
in one of the	7
turns out to be	7
for infectious disease transmission	7
error and attack tolerance	7
in the control condition	7
characterized by a high	7
the characteristics of the	7
for the training of	7
be attributed to the	7
holder for this preprint	7
proteins in the human	7
the relationships between the	7
it is easy to	7
the design of the	7
frequency of visits to	7
power law degree distribution	7
for perinatal mood and	7
size of the input	7
authors proposed a new	7
of financing and innovation	7
it is worth mentioning	7
infectious diseases of humans	7
on behalf of the	7
stored in the cloud	7
in the development of	7
of the united states	7
the copyright holder for	7
for the treatment of	7
of proteins targeted by	7
follows a power law	7
to improve the performance	7
but also on the	7
a contribution to the	7
on the top of	7
are given in section	7
complex network representation learning	7
of nodes in a	7
mood and anxiety disorders	7
on the special path	7
for the dynamics of	7
the real interpersonal social	7
positive relationship between eo	7
reduce the number of	7
the second half of	7
which can be used	7
the estimation of the	7
the spread of an	7
the number of classes	7
we make use of	7
in the dual space	7
our understanding of the	7
copyright holder for this	7
is worth mentioning that	7
deep convolutional neural networks	7
one of the first	7
in the supply chain	7
from the fact that	7
samples at d p	7
the level of clustering	7
used to measure the	6
also be noted that	6
or exceeds the permitted	6
the peak of infection	6
the lifetime of the	6
is defined as where	6
and social network ties	6
of the quality of	6
it is impossible to	6
where this figure has	6
to detection or extinction	6
as a tool for	6
spread of infectious disease	6
we can see that	6
the heterogeneity of the	6
of social networks in	6
the fraction of the	6
through the lens of	6
been applied to the	6
is calculated and subtracted	6
the inverse of the	6
the full hm model	6
of node i is	6
visual analytics for high	6
of the random network	6
in one of two	6
the average degree is	6
the creation of a	6
of the tail risk	6
to the other two	6
and the amount of	6
increase the risk of	6
been used in the	6
authors declare no conflict	6
the medical masks dataset	6
take into account the	6
centrality of a node	6
when the number of	6
we also found that	6
for the cp model	6
degree of each node	6
t x and f	6
is defined as follows	6
dynamical processes on networks	6
be one of the	6
network actors and network	6
in the data set	6
a finite fraction of	6
density of infected nodes	6
of the population is	6
knowledge of the network	6
increasing the number of	6
the relu activation function	6
supported by the national	6
is represented by a	6
of the original network	6
to the performance of	6
when compared to the	6
we will focus on	6
shed light on the	6
it should also be	6
the directed validated network	6
neutral with regard to	6
the effects of social	6
it is likely that	6
the knowledge structure of	6
as the sum of	6
this figure has been	6
the first and second	6
average path length of	6
permission directly from the	6
the frequency of detection	6
contribution to the mathematical	6
shows the results of	6
width or length no	6
in the present work	6
the initial number of	6
which the number of	6
wireless sensor networks is	6
maternal and child health	6
this work was supported	6
the context data stream	6
attack tolerance of complex	6
can be modeled as	6
the spectral radius of	6
the difference in the	6
the number of new	6
or equal to the	6
the selection of the	6
to obtain permission directly	6
medicine discovery network viral	6
in a network is	6
strength of weak ties	6
the social network of	6
figure shows the results	6
social network analysis of	6
was carried out to	6
before and during the	6
with the network structure	6
the enron email network	6
can be attributed to	6
of a large number	6
is not possible to	6
springer nature remains neutral	6
the epidemic threshold of	6
second half of the	6
as input for the	6
if the total number	6
the framingham heart study	6
it is observed that	6
and control of diseases	6
care medicine discovery network	6
the amount of information	6
to solve the problem	6
the posterior probability distribution	6
the time of the	6
in the same way	6
network size in humans	6
with deep convolutional neural	6
spread of the virus	6
we focus on the	6
or length no more	6
the identifi cation of	6
of each of the	6
higher social media use	6
a node with a	6
of susceptible nodes is	6
in the first case	6
is given by a	6
to focus on the	6
networks and global change	6
birth and death of	6
of the contact networks	6
and one of the	6
the distance between the	6
that it is possible	6
were included in the	6
of the human brain	6
for the ashwagandha network	6
of the complex network	6
based on data from	6
this is an important	6
with a probability of	6
used in this study	6
supply chain disruption propagation	6
path length of a	6
the formation of the	6
time to detection or	6
play a role in	6
taken into account in	6
of three or more	6
the single shot object	6
become one of the	6
of neural networks with	6
have been proposed for	6
creative commons license and	6
nature remains neutral with	6
learning for image recognition	6
network as a whole	6
for a network with	6
as well as its	6
investigate the evolution of	6
in terms of number	6
order to evaluate the	6
and the leadership team	6
figure has been adapted	6
from the copyright holder	6
you will need to	6
in order to analyze	6
in the network with	6
worth mentioning that the	6
networking in the public	6
length no more than	6
community structure in social	6
a number of studies	6
exceeds the permitted use	6
maps and institutional affiliations	6
be regarded as a	6
need to obtain permission	6
and social network size	6
the average of the	6
the strength of weak	6
of the supplementary material	6
in the era of	6
not permitted by statutory	6
was found to be	6
the mechanism of action	6
will need to obtain	6
the actual relation ratio	6
in networks with strong	6
public staying away from	6
is not permitted by	6
as a source of	6
of these networks are	6
less likely to be	6
residual learning for image	6
the network is not	6
tend to be more	6
condition for the spreading	6
in the following section	6
claims in published maps	6
the creative commons attribution	6
higher pagerank in old	6
of the hm model	6
a small fraction of	6
eigenvalue of the adjacency	6
between the number of	6
the scale of the	6
with the use of	6
packaging type specific volume	6
of different sizes and	6
the same set of	6
order to analyze the	6
set of measures of	6
the positive relationship between	6
birds of a feather	6
that are based on	6
spreading in complex networks	6
a network is the	6
mathematical theory of infectious	6
n nodes in the	6
sis and cp models	6
for the spreading of	6
with a number of	6
in complex networks with	6
presence of demographic cues	6
of ly e in	6
in agreement with the	6
regulation or exceeds the	6
with the results of	6
of influential spreaders in	6
measures of centrality based	6
can be explained by	6
between anxiety and depression	6
detection and tracking of	6
is much smaller than	6
actors in the network	6
the level of the	6
from the point of	6
in the center of	6
the number of kernels	6
th time step in	6
of this article is	6
the details of the	6
of the relationship between	6
the correlation between the	6
in order to get	6
use is not permitted	6
by west et al	6
gated channel type scn	6
on the use of	6
obtain permission directly from	6
to each other than	6
in order to avoid	6
have the potential to	6
each node can be	6
be related to the	6
that the neural network	6
of the creative commons	6
adjacency matrix of the	6
the scope of this	6
to the rest of	6
the semantics of the	6
node i at time	6
the architecture of the	6
in the level of	6
to jurisdictional claims in	6
channel type scn a	6
a case study of	6
of measures of centrality	6
it is not possible	6
initial growth rate is	6
are part of the	6
remains neutral with regard	6
induced by aconitine alkaloids	6
is a kind of	6
as we will see	6
in the influence of	6
this study is to	6
number of susceptible individuals	6
deep residual learning for	6
a dynamical process on	6
processes on complex networks	6
is assumed to be	6
diseases in networks with	6
of the size of	6
actors and network ties	6
one of the earliest	6
not depend on the	6
staying away from hospitals	6
the presence of homophily	6
be seen in figure	6
from where this figure	6
are considered to be	6
take advantage of the	6
as the basis for	6
on the epidemic threshold	6
the growth of the	6
nodes in a graph	6
proposed or protocol has	6
with the aim of	6
cardiotoxicity induced by aconitine	6
the spread of awareness	6
on the role of	6
difference between the two	6
of node i in	6
the position of the	6
type scn a sodium	6
the role of a	6
of a node is	6
the frequency with which	6
of the input data	6
communities in large networks	6
community detection in networks	6
spreading processes in multilayer	6
each of the three	6
as depicted in fig	6
statutory regulation or exceeds	6
with the size of	6
solve the problem of	6
x and f d	6
the degree to which	6
the effectiveness of the	6
pagerank in old at	6
theory of infectious diseases	6
except for the final	6
spreading of the disease	6
regard to jurisdictional claims	6
of the epidemic spread	6
probability of an outbreak	6
also been used to	6
with regard to jurisdictional	6
the composition of the	6
critical care medicine discovery	6
be seen that the	6
networks with strong community	6
of centrality based on	6
are less likely to	6
are related to the	6
evaluate the impact of	6
at the cost of	6
directly from the copyright	6
the proposed sr network	6
the start of the	6
transmission dynamics and control	6
nodes in each layer	6
the parameters of the	6
tolerance of complex networks	6
on the evolution of	6
of different types of	6
published maps and institutional	6
the temporal evolution of	6
vertex entity mask task	6
as well as their	6
are widely used in	6
not take into account	6
the number of neurons	6
global networks and global	6
processes in multilayer networks	6
in the structure of	6
we can find the	6
of the port community	6
it is the case	6
of a social network	6
network of proteins targeted	6
the presence or absence	6
permitted by statutory regulation	6
that are associated with	6
number of doses per	6
a set of measures	6
finite fraction of the	6
is more likely to	6
be defined as a	6
the network structure is	6
of the ppi network	6
the probability distribution of	6
to the lack of	6
these networks can be	6
network is shown in	6
movement data of iot	6
the number of shortest	6
single shot object detectors	6
of an infectious disease	6
role of social networks	6
the weak commutation condition	6
the study assistance network	6
half of the eighteenth	6
of human proteins targeted	6
the structural properties of	6
as the average number	6
spectral radius of the	6
at the university of	6
have been made to	6
seems to be a	6
centrality based on betweenness	6
in the formation of	6
control of diseases in	6
there is a need	6
decreases by a factor	6
and the frequency of	6
jurisdictional claims in published	6
the same size as	6
in the eighteenth century	6
convolutional neural networks for	6
doses per unit time	6
number of common alters	6
is part of the	6
in an attempt to	6
national institutes of health	6
the goal is to	6
the development of a	6
for the presence of	6
technological forecasting social change	6
an edge between two	6
his research interests include	6
for the purposes of	6
the proposed selfrl is	6
number of infectious individuals	6
the frequency of visits	6
in published maps and	6
by statutory regulation or	6
the best method for	6
are assumed to be	6
nodes in complex networks	6
the time evolution of	6
declare no conflict of	6
and during the covid	6
it is known that	6
in our understanding of	6
in the analysis of	6
in the sir model	6
to changes in the	6
denotes the number of	6
to each of the	6
level structure and semantic	6
the shortest paths between	6
one of the two	6
in the network are	6
in the machine learning	6
also known as the	6
in the road network	5
and the embeddedness of	5
a generalization of the	5
orbital prefrontal cortex volume	5
the mean degree of	5
influential friends of friends	5
a connected and undirected	5
the results of our	5
the first stage of	5
unless indicated otherwise in	5
the ideas of the	5
loan guarantee network risk	5
power law degree distributions	5
the initial growth rate	5
wavelet based denoising of	5
is also known as	5
the periphery of the	5
size as the input	5
of the aconitine alkaloids	5
as long as you	5
the basic reproductive ratio	5
nodes refer to partners	5
properties of the network	5
third party material in	5
the time scale separation	5
refer to operations of	5
a new approach for	5
the katrina response network	5
nodes refer to leadership	5
this study are available	5
underlying structure of the	5
we now turn to	5
the number of users	5
we assume that a	5
the wake of the	5
of isolation rate in	5
the other focal firm	5
due to the large	5
of the jacobian matrix	5
an interactive email model	5
information about the health	5
in the latent space	5
the number of proteins	5
the national institute of	5
s in the supplementary	5
acoustic signals generated by	5
with deep neural networks	5
is defined as a	5
refer to leadership of	5
gives an overview of	5
the result shows that	5
the expected internal relation	5
nodes in the two	5
in the work by	5
in the areas of	5
epidemic dynamics on an	5
multiple omics data types	5
in the network were	5
the first of the	5
number of healthy nodes	5
dynamics of information diffusion	5
is important to notice	5
a deeper understanding of	5
can be a useful	5
function ks p i	5
the sr network is	5
the sectoral tail risk	5
for the evaluation of	5
both investors and researchers	5
the mean of the	5
undirected version of the	5
number of studies have	5
classification with deep convolutional	5
has been suggested that	5
the global transmission of	5
in order to observe	5
the sis and cp	5
the minimum number of	5
if material is not	5
was found that the	5
in the dynamics of	5
the influence of genes	5
after passing through the	5
of the antwerp port	5
long as you give	5
a significant reduction in	5
the effects of a	5
be more likely to	5
nodes refer to operations	5
the innate immune response	5
has been widely used	5
terrains of different sizes	5
if and only if	5
it has been suggested	5
is viewed as a	5
models of disease transmission	5
and the actual relation	5
the ratio between the	5
parts of the network	5
number of doses is	5
result shows that the	5
the integration of iot	5
with low trust propensity	5
the propagation of the	5
a network of networks	5
we can observe that	5
the final identification accuracy	5
of doses per unit	5
a certain number of	5
in the wake of	5
any medium or format	5
number of contacts per	5
in vitro and in	5
one can see that	5
from a network perspective	5
can be carried out	5
by a set of	5
the context of a	5
in this study is	5
network and bridge centrality	5
of the proposed model	5
of the impact of	5
and community structure in	5
for mitigating an influenza	5
the nature of these	5
of neural networks in	5
opportunistic routing algorithm for	5
networks the structure and	5
about the health status	5
in order to reduce	5
most of the network	5
the extension of the	5
between subjects and friends	5
to the creative commons	5
indicated otherwise in a	5
functional edge ratio of	5
the inputs of the	5
be applied to a	5
and backward disruption diffusion	5
is very difficult to	5
the mean infection level	5
research focus parallelship network	5
function of the network	5
the one hand and	5
of such an approach	5
the period of data	5
based on the above	5
the indian institute of	5
play a crucial role	5
to adopt preventive behavior	5
we investigate the evolution	5
this is done by	5
in the previous subsection	5
for the three integrators	5
is associated with a	5
three or more entities	5
the dependence of the	5
whether or not to	5
as a case study	5
two different types of	5
the ratio of white	5
at time t to	5
temporal sequence networks of	5
used to evaluate the	5
sectoral tail risk network	5
the temporal nature of	5
on the structure and	5
modeling and analysis of	5
the sake of simplicity	5
the rapid development of	5
when there is no	5
of the markov chain	5
secondary infections caused by	5
and the value of	5
the same as in	5
speed of the epidemic	5
in the royal decree	5
shown in table and	5
to understand how the	5
to solve this problem	5
and structure of the	5
the performance of their	5
with the same size	5
the average value of	5
with some of the	5
the role of individual	5
of health and welfare	5
we provide an overview	5
cascaded convolutional neural network	5
by the italian government	5
refer to partners of	5
networks the effect of	5
the change of the	5
the context of big	5
threshold models of collective	5
smaller than or equal	5
the size of a	5
it is expected that	5
incubation and infectious periods	5
be a useful tool	5
some of them are	5
which is equal to	5
of individuals in the	5
could be due to	5
development of the road	5
a strong community structure	5
as soon as the	5
this is not a	5
the transfer of knowledge	5
to be connected to	5
reducing the number of	5
pick a random node	5
i at time t	5
evolution of an innovation	5
the dynamic evolution of	5
with the exception of	5
the total population size	5
at the base station	5
provide a link to	5
due to the high	5
connected to one another	5
the course of an	5
the set of all	5
and the results are	5
in the validated network	5
be assigned to the	5
outcome of the algorithm	5
the first part of	5
nodes with degree d	5
the human ppi network	5
the enron email dataset	5
we consider that the	5
are used for the	5
indicate if changes were	5
the basis of the	5
such as the one	5
indian institute of technology	5
the case for the	5
the convolutional neural network	5
aspects of the network	5
to the probability of	5
and inability to relax	5
more attention to the	5
online interpersonal social network	5
of infected individuals in	5
of inputs and outputs	5
a functional edge ratio	5
the mobility of the	5
wearing conditions with high	5
the preferential attachment model	5
for genes with higher	5
a large body of	5
for the effects of	5
can lead to a	5
the nodes of the	5
mainly due to the	5
in terms of a	5
both positive and negative	5
of the laplacian matrix	5
approach is based on	5
based on node value	5
a case study on	5
in chinese stock market	5
map by the authors	5
like to thank the	5
as soon as possible	5
and the other two	5
based on random walks	5
changes in the influence	5
reproduction in any medium	5
a vital role in	5
despite the fact that	5
models of collective behavior	5
simulate the effects of	5
connected component of the	5
of infected in the	5
at the regional level	5
credit to the original	5
the last two decades	5
networks for emotional profiling	5
the goal of the	5
the effectiveness of a	5
of shortest paths between	5
length of a network	5
due to the lack	5
the critical behavior of	5
from i to j	5
to the original author	5
data from social media	5
and your intended use	5
as you give appropriate	5
and the nature of	5
a high trust propensity	5
to have the same	5
this work is supported	5
is depicted in fig	5
of the data set	5
by a small number	5
granularity complex network representation	5
data of iot devices	5
of the topological structure	5
were found to be	5
on top of the	5
strategies for mitigating an	5
a critical role in	5
plays a crucial role	5
in the population and	5
as in the case	5
a standard deviation of	5
in the forwarder list	5
of the network by	5
in comparison to the	5
of a network is	5
in the primal space	5
can serve as a	5
between eo and financial	5
be used to determine	5
can be very useful	5
in the next step	5
and reproduction in any	5
this has led to	5
a neural network model	5
unfolding of communities in	5
position in the network	5
and mental health services	5
and financing is high	5
over a period of	5
role in the network	5
due to the covid	5
do not have a	5
of the neural networks	5
embeddedness of individual actors	5
to more than one	5
patients with coronavirus disease	5
have been shown to	5
the relationship between entrepreneurial	5
can be calculated by	5
cargo traffic and passenger	5
a change in the	5
actual relation ratio is	5
of a node i	5
number of deg between	5
in the following sections	5
in heterogeneous biomedical networks	5
final misinformation outbreak size	5
an approximation of the	5
and the need to	5
the performance of blockchains	5
of the model and	5
directions for future research	5
the number of days	5
the focus of this	5
the human protein atlas	5
infectious diseases and its	5
fast unfolding of communities	5
and indicate if changes	5
of artificial neural networks	5
network pharmacology approach to	5
paths that pass through	5
by the total number	5
critical behavior of the	5
the product of the	5
neural networks can be	5
performance of the reference	5
in the contact network	5
the final misinformation outbreak	5
the probability of infection	5
the relevance of the	5
if there is no	5
an analysis of the	5
understood in terms of	5
network as well as	5
which is the number	5
this paper is to	5
the egos in the	5
guarantee network risk management	5
of the editorial board	5
of a dynamic environment	5
will be assigned to	5
in the port community	5
association between eo and	5
transcriptomic and proteomic data	5
of the three networks	5
does not affect the	5
and its relationship with	5
at the global level	5
is affected by the	5
have found that the	5
networks epidemic processes in	5
for the success of	5
outside the training set	5
that allows us to	5
number of new infections	5
home and personal items	5
should also be noted	5
in order to be	5
of the dynamics of	5
is illustrated in fig	5
generated by the drones	5
to improve the accuracy	5
process of virus propagation	5
packing of granular material	5
structure in social and	5
of this algorithm is	5
a comparison of the	5
the number of deg	5
in social and biological	5
signals generated by the	5
red nodes refer to	5
from the minet package	5
does not depend on	5
of the reference model	5
expected internal relation ratio	5
diseases and its applications	5
intended use is not	5
in complex networks the	5
from left to right	5
the coronavirus disease pandemic	5
in the hospitality industry	5
and a set of	5
coupling of financing and	5
otherwise in a credit	5
when the largest eigenvalue	5
is known about the	5
for old and young	5
as a way to	5
to name a few	5
party material in this	5
degree and betweenness centrality	5
in the social network	5
and the generation of	5
the first and the	5
to a large number	5
is not the case	5
the paper is structured	5
the knowledge of the	5
context of big data	5
at the core of	5
networking with a researcher	5
at time t and	5
the node degree distribution	5
it would be interesting	5
the effects of various	5
of the first and	5
of packing of granular	5
as a starting point	5
on an adaptive network	5
each node represents a	5
structure and semantic information	5
network theory and sars	5
in any medium or	5
and right wing parties	5
as we will show	5
can be used in	5
purple nodes refer to	5
signal to noise ratio	5
of a network of	5
are based on the	5
terms of number of	5
used to calculate the	5
note that in the	5
the starting point for	5
which has been shown	5
the dynamics of infectious	5
of action of the	5
similar to the one	5
be used in the	5
the most widely used	5
a limited number of	5
with the integration of	5
the spread of misinformation	5
the jacobian matrix is	5
yellow nodes refer to	5
this allows us to	5
sizes and spatial characteristics	5
in the sense of	5
belonging to the same	5
which may not be	5
the steel blue community	5
quality of the community	5
based on random walk	5
and child health network	5
network for infectious disease	5
the identification of the	5
backward disruption diffusion rates	5
that coupling is affected	5
can be understood in	5
between depression and anxiety	5
the evolution of an	5
as a model for	5
work was supported by	5
to have a more	5
hypothesis is that coupling	5
of different immunization strategies	5
gene set enrichment analysis	5
in order to better	5
results indicate that the	5
the final size of	5
of the members of	5
between each pair of	5
of the sir model	5
the coupling between innovation	5
the authors focused on	5
a model for the	5
the latent space is	5
response to the covid	5
for the conservation of	5
a function of time	5
study of infectious diseases	5
the fraction of coupling	5
the scalability of blockchains	5
the goal of this	5
node in a network	5
it is defined as	5
top most variable genes	5
for the random network	5
an increasing number of	5
the emotional profile of	5
be defined as the	5
t x and t	5
energy of the network	5
but it is not	5
for early detection of	5
the objective is to	5
the number of healthy	5
after each round of	5
note springer nature remains	5
have the same number	5
between pairs of nodes	5
but it is also	5
of the disease in	5
for a total of	5
can be modeled by	5
in the entire network	5
to reach a steady	5
is the case for	5
x and t d	5
in the human protein	5
in the direction of	5
the name of the	5
this is not the	5
to contribute to the	5
complex network theory can	5
for a variety of	5
are given in appendix	5
von mering et al	5
the idea is that	5
the number of steps	5
it is not clear	5
see galaz et al	5
of our neural networks	5
in humans and animals	5
performance in terms of	5
need to be immunized	5
the condition for the	5
methods have been proposed	5
social media use on	5
the network is trained	5
a wide number of	5
rest of the network	5
can be identified by	5
can be observed in	5
to the steady state	5
where m is the	5
your intended use is	5
old and young cells	5
to be involved in	5
in the two layers	5
are associated with the	5
radial structure of the	5
a new generation of	5
divided by the total	5
other third party material	5
maximization in social networks	5
is smaller than or	5
network in terms of	5