Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularizing Semi-supervised Graph Convolutional Networks with a
  Manifold Smoothness Loss   Existing graph convolutional networks focus on the neighborhood aggregation
scheme. When applied to semi-supervised learning, they often suffer from the
overfitting problem as the networks are trained with the cross-entropy loss on
a small potion of labeled data. In this paper, we propose an unsupervised
manifold smoothness loss defined with respect to the graph structure, which can
be added to the loss function as a regularization. We draw connections between
the proposed loss with an iterative diffusion process, and show that minimizing
the loss is equivalent to aggregate neighbor predictions with infinite layers.
We conduct experiments on multi-layer perceptron and existing graph networks,
and demonstrate that adding the proposed loss can improve the performance
consistently.

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The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph