Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Learning Convolutional Neural Network Approach for Network Robustness
  Prediction   Network robustness is critical for various societal and industrial networks
again malicious attacks. In particular, connectivity robustness and
controllability robustness reflect how well a networked system can maintain its
connectedness and controllability against destructive attacks, which can be
quantified by a sequence of values that record the remaining connectivity and
controllability of the network after a sequence of node- or edge-removal
attacks. Traditionally, robustness is determined by attack simulations, which
are computationally very time-consuming or even practically infeasible. In this
paper, an improved method for network robustness prediction is developed based
on learning feature representation using convolutional neural network
(LFR-CNN). In this scheme, higher-dimensional network data are compressed to
lower-dimensional representations, and then passed to a CNN to perform
robustness prediction. Extensive experimental studies on both synthetic and
real-world networks, both directed and undirected, demonstrate that 1) the
proposed LFR-CNN performs better than other two state-of-the-art prediction
methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive
to the variation of the network size, which significantly extends its
applicability; 3) although LFR-CNN needs more time to perform feature learning,
it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN
not only can accurately predict network robustness, but also provides a good
indicator for connectivity robustness, better than the classical spectral
measures.

13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn