Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Robust Are Graph Neural Networks to Structural Noise?   Graph neural networks (GNNs) are an emerging model for learning graph
embeddings and making predictions on graph structured data. However, robustness
of graph neural networks is not yet well-understood. In this work, we focus on
node structural identity predictions, where a representative GNN model is able
to achieve near-perfect accuracy. We also show that the same GNN model is not
robust to addition of structural noise, through a controlled dataset and set of
experiments. Finally, we show that under the right conditions, graph-augmented
training is capable of significantly improving robustness to structural noise.

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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin