Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimization and Generalization Analysis of Transduction through
  Gradient Boosting and Application to Multi-scale Graph Neural Networks   It is known that the current graph neural networks (GNNs) are difficult to
make themselves deep due to the problem known as over-smoothing. Multi-scale
GNNs are a promising approach for mitigating the over-smoothing problem.
However, there is little explanation of why it works empirically from the
viewpoint of learning theory. In this study, we derive the optimization and
generalization guarantees of transductive learning algorithms that include
multi-scale GNNs. Using the boosting theory, we prove the convergence of the
training error under weak learning-type conditions. By combining it with
generalization gap bounds in terms of transductive Rademacher complexity, we
show that a test error bound of a specific type of multi-scale GNNs that
decreases corresponding to the number of node aggregations under some
conditions. Our results offer theoretical explanations for the effectiveness of
the multi-scale structure against the over-smoothing problem. We apply boosting
algorithms to the training of multi-scale GNNs for real-world node prediction
tasks. We confirm that its performance is comparable to existing GNNs, and the
practical behaviors are consistent with theoretical observations. Code is
available at https://github.com/delta2323/GB-GNN.

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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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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: cmp-lg