Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-view Self-Supervised Learning on Heterogeneous Graph Neural
  Network via Bootstrapping   Heterogeneous graph neural networks can represent information of
heterogeneous graphs with excellent ability. Recently, self-supervised learning
manner is researched which learns the unique expression of a graph through a
contrastive learning method. In the absence of labels, this learning methods
show great potential. However, contrastive learning relies heavily on positive
and negative pairs, and generating high-quality pairs from heterogeneous graphs
is difficult. In this paper, in line with recent innovations in self-supervised
learning called BYOL or bootstrapping, we introduce a that can generate good
representations without generating large number of pairs. In addition, paying
attention to the fact that heterogeneous graphs can be viewed from two
perspectives, network schema and meta-path views, high-level expressions in the
graphs are captured and expressed. The proposed model showed state-of-the-art
performance than other methods in various real world datasets.

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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an