Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interlayer Link Prediction in Multiplex Social Networks Based on
  Multiple Types of Consistency between Embedding Vectors   Online users are typically active on multiple social media networks (SMNs),
which constitute a multiplex social network. It is becoming increasingly
challenging to determine whether given accounts on different SMNs belong to the
same user; this can be expressed as an interlayer link prediction problem in a
multiplex network. To address the challenge of predicting interlayer links ,
feature or structure information is leveraged. Existing methods that use
network embedding techniques to address this problem focus on learning a
mapping function to unify all nodes into a common latent representation space
for prediction; positional relationships between unmatched nodes and their
common matched neighbors (CMNs) are not utilized. Furthermore, the layers are
often modeled as unweighted graphs, ignoring the strengths of the relationships
between nodes. To address these limitations, we propose a framework based on
multiple types of consistency between embedding vectors (MulCEV). In MulCEV,
the traditional embedding-based method is applied to obtain the degree of
consistency between the vectors representing the unmatched nodes, and a
proposed distance consistency index based on the positions of nodes in each
latent space provides additional clues for prediction. By associating these two
types of consistency, the effective information in the latent spaces is fully
utilized. Additionally, MulCEV models the layers as weighted graphs to obtain
representation. In this way, the higher the strength of the relationship
between nodes, the more similar their embedding vectors in the latent
representation space will be. The results of our experiments on several
real-world datasets demonstrate that the proposed MulCEV framework markedly
outperforms current embedding-based methods, especially when the number of
training iterations is small.

8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph