Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforced Causal Explainer for Graph Neural Networks   Explainability is crucial for probing graph neural networks (GNNs), answering
questions like "Why the GNN model makes a certain prediction?". Feature
attribution is a prevalent technique of highlighting the explanatory subgraph
in the input graph, which plausibly leads the GNN model to make its prediction.
Various attribution methods exploit gradient-like or attention scores as the
attributions of edges, then select the salient edges with top attribution
scores as the explanation. However, most of these works make an untenable
assumption - the selected edges are linearly independent - thus leaving the
dependencies among edges largely unexplored, especially their coalition effect.
We demonstrate unambiguous drawbacks of this assumption - making the
explanatory subgraph unfaithful and verbose. To address this challenge, we
propose a reinforcement learning agent, Reinforced Causal Explainer
(RC-Explainer). It frames the explanation task as a sequential decision process
- an explanatory subgraph is successively constructed by adding a salient edge
to connect the previously selected subgraph. Technically, its policy network
predicts the action of edge addition, and gets a reward that quantifies the
action's causal effect on the prediction. Such reward accounts for the
dependency of the newly-added edge and the previously-added edges, thus
reflecting whether they collaborate together and form a coalition to pursue
better explanations. As such, RC-Explainer is able to generate faithful and
concise explanations, and has a better generalization power to unseen graphs.
When explaining different GNNs on three graph classification datasets,
RC-Explainer achieves better or comparable performance to SOTA approaches
w.r.t. predictive accuracy and contrastivity, and safely passes sanity checks
and visual inspections. Codes are available at
https://github.com/xiangwang1223/reinforced_causal_explainer.

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The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-alg
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph