Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge
  Graph   Knowledge graphs (KGs) composed of users, objects, and tags are widely used
in web applications ranging from E-commerce, social media sites to news
portals. This paper concentrates on an attractive application which aims to
predict the object-tag links in the KG for better tag recommendation and object
explanation. When predicting the object-tag links, both the first-order and
high-order proximities between entities in the KG propagate essential
similarity information for better prediction. Most existing methods focus on
preserving the first-order proximity between entities in the KG. However, they
cannot capture the high-order proximities in an explicit way, and the adopted
margin-based criterion cannot measure the first-order proximity on the global
structure accurately. In this paper, we propose a novel approach named Dual
Graph Embedding (DGE) that models both the first-order and high-order
proximities in the KG via an auto-encoding architecture to facilitate better
object-tag relation inference. Here the dual graphs contain an object graph and
a tag graph that explicitly depict the high-order object-object and tag-tag
proximities in the KG. The dual graph encoder in DGE then encodes these
high-order proximities in the dual graphs into entity embeddings. The decoder
formulates a skip-gram objective that maximizes the first-order proximity
between observed object-tag pairs over the global proximity structure. With the
supervision of the decoder, the embeddings derived by the encoder will be
refined to capture both the first-order and high-order proximities in the KG
for better link prediction. Extensive experiments on three real-world datasets
demonstrate that DGE outperforms the state-of-the-art methods.

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The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin