Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepGraph: Graph Structure Predicts Network Growth   The topological (or graph) structures of real-world networks are known to be
predictive of multiple dynamic properties of the networks. Conventionally, a
graph structure is represented using an adjacency matrix or a set of
hand-crafted structural features. These representations either fail to
highlight local and global properties of the graph or suffer from a severe loss
of structural information. There lacks an effective graph representation, which
hinges the realization of the predictive power of network structures.
  In this study, we propose to learn the represention of a graph, or the
topological structure of a network, through a deep learning model. This
end-to-end prediction model, named DeepGraph, takes the input of the raw
adjacency matrix of a real-world network and outputs a prediction of the growth
of the network. The adjacency matrix is first represented using a graph
descriptor based on the heat kernel signature, which is then passed through a
multi-column, multi-resolution convolutional neural network. Extensive
experiments on five large collections of real-world networks demonstrate that
the proposed prediction model significantly improves the effectiveness of
existing methods, including linear or nonlinear regressors that use
hand-crafted features, graph kernels, and competing deep learning methods.

29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat