Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Uncoupled Training Architecture for Large Graph Learning   Graph Convolutional Network (GCN) has been widely used in graph learning
tasks. However, GCN-based models (GCNs) is an inherently coupled training
framework repetitively conducting the complex neighboring aggregation, which
leads to the limitation of flexibility in processing large-scale graph. With
the depth of layers increases, the computational and memory cost of GCNs grow
explosively due to the recursive neighborhood expansion. To tackle these
issues, we present Node2Grids, a flexible uncoupled training framework that
leverages the independent mapped data for obtaining the embedding. Instead of
directly processing the coupled nodes as GCNs, Node2Grids supports a more
efficacious method in practice, mapping the coupled graph data into the
independent grid-like data which can be fed into the efficient Convolutional
Neural Network (CNN). This simple but valid strategy significantly saves memory
and computational resource while achieving comparable results with the leading
GCN-based models. Specifically, by ranking each node's influence through
degree, Node2Grids selects the most influential first-order as well as
second-order neighbors with central node fusion information to construct the
grid-like data. For further improving the efficiency of downstream tasks, a
simple CNN-based neural network is employed to capture the significant
information from the mapped grid-like data. Moreover, the grid-level attention
mechanism is implemented, which enables implicitly specifying the different
weights for neighboring nodes with different influences. In addition to the
typical transductive and inductive learning tasks, we also verify our framework
on million-scale graphs to demonstrate the superiority of the proposed
Node2Grids model against the state-of-the-art GCN-based approaches.

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The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con