Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Learning Graph Representations   Mining graph data has become a popular research topic in computer science and
has been widely studied in both academia and industry given the increasing
amount of network data in the recent years. However, the huge amount of network
data has posed great challenges for efficient analysis. This motivates the
advent of graph representation which maps the graph into a low-dimension vector
space, keeping original graph structure and supporting graph inference. The
investigation on efficient representation of a graph has profound theoretical
significance and important realistic meaning, we therefore introduce some basic
ideas in graph representation/network embedding as well as some representative
models in this chapter.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga