Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Graph Neural Network Explanation   Graph Neural Networks achieve remarkable results on problems with structured
data but come as black-box predictors. Transferring existing explanation
techniques, such as occlusion, fails as even removing a single node or edge can
lead to drastic changes in the graph. The resulting graphs can differ from all
training examples, causing model confusion and wrong explanations. Thus, we
argue that explicability must use graphs compliant with the distribution
underlying the training data. We coin this property Distribution Compliant
Explanation (DCE) and present a novel Contrastive GNN Explanation (CoGE)
technique following this paradigm. An experimental study supports the efficacy
of CoGE.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: gr-qc