Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Importance of Sampling in Training GCNs: Tighter Analysis and
  Variance Reduction   Graph Convolutional Networks (GCNs) have achieved impressive empirical
advancement across a wide variety of semi-supervised node classification tasks.
Despite their great success, training GCNs on large graphs suffers from
computational and memory issues. A potential path to circumvent these obstacles
is sampling-based methods, where at each layer a subset of nodes is sampled.
Although recent studies have empirically demonstrated the effectiveness of
sampling-based methods, these works lack theoretical convergence guarantees
under realistic settings and cannot fully leverage the information of evolving
parameters during optimization. In this paper, we describe and analyze a
general doubly variance reduction schema that can accelerate any sampling
method under the memory budget. The motivating impetus for the proposed schema
is a careful analysis of the variance of sampling methods where it is shown
that the induced variance can be decomposed into node embedding approximation
variance (zeroth-order variance) during forward propagation and
layerwise-gradient variance (first-order variance) during backward propagation.
We theoretically analyze the convergence of the proposed schema and show that
it enjoys an $\mathcal{O}(1/T)$ convergence rate. We complement our theoretical
results by integrating the proposed schema in different sampling methods and
applying them to different large real-world graphs.

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The categories of this paper are: physics
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
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
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: math