Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autonomous inference of complex network dynamics from incomplete and
  noisy data   The availability of empirical data that capture the structure and behavior of
complex networked systems has been greatly increased in recent years, however a
versatile computational toolbox for unveiling a complex system's nodal and
interaction dynamics from data remains elusive. Here we develop a two-phase
approach for autonomous inference of complex network dynamics, and its
effectiveness is demonstrated by the tests of inferring neuronal, genetic,
social, and coupled oscillators dynamics on various synthetic and real
networks. Importantly, the approach is robust to incompleteness and noises,
including low resolution, observational and dynamical noises, missing and
spurious links, and dynamical heterogeneity. We apply the two-phase approach to
inferring the early spreading dynamics of H1N1 flu upon the worldwide airline
network, and the inferred dynamical equation can also capture the spread of
SARS and COVID-19 diseases. These findings together offer an avenue to discover
the hidden microscopic mechanisms of a broad array of real networked systems.

28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph