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Found 7 papers, 4 papers with code
Reinforced Causal Explainer for Graph Neural Networks
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.
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
Deconfounding to Explanation Evaluation in Graph Neural Networks
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
Towards Multi-Grained Explainability for Graph Neural Networks
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
Causal Screening to Interpret Graph Neural Networks
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
Disentangled Graph Collaborative Filtering
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
