Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BiasedWalk: Learning Global-aware Node Embeddings via Biased Sampling   Popular node embedding methods such as DeepWalk follow the paradigm of
performing random walks on the graph, and then requiring each node to be
proximate to those appearing along with it. Though proved to be successful in
various tasks, this paradigm reduces a graph with topology to a set of
sequential sentences, thus omitting global information. To produce global-aware
node embeddings, we propose BiasedWalk, a biased random walk strategy that
favors nodes with similar semantics. Empirical evidence suggests BiasedWalk can
generally enhance global awareness of the generated embeddings.

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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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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: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin