Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Network Embeddings for Improving Network Alignment   Network (or Graph) Alignment Algorithms aims to reveal structural
similarities among graphs. In particular Local Network Alignment Algorithms
(LNAs) finds local regions of similarity among two or more networks. Such
algorithms are in general based on a set of seed nodes that are used to grow an
alignment. Almost all LNAs algorithms use as seed nodes a set of vertices based
on context information (e.g. a set of biologically related in biological
network alignment) and this may cause a bias or a data-circularity problem.
More recently, we demonstrated that the use of topological information in the
choice of seed nodes may improve the quality of the alignments. We used some
common approaches based on global alignment algorithms for capturing
topological similarity among nodes. In parallel, it has been demonstrated that
the use of network embedding methods (or representation learning), may capture
the structural similarity among nodes better than other methods. Therefore we
propose to use network embeddings to learn structural similarity among nodes
and to use such similarity to improve LNA extendings our previous algorithms.
We define a framework for LNA.

37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat