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Trustworthy graph neural networks: Aspects, methods, and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …
methods for diverse real-world scenarios, ranging from daily applications such as …
Dag matters! gflownets enhanced explainer for graph neural networks
Uncovering rationales behind predictions of graph neural networks (GNNs) has received
increasing attention over the years. Existing literature mainly focus on selecting a subgraph …
increasing attention over the years. Existing literature mainly focus on selecting a subgraph …
Explaining graph neural networks via structure-aware interaction index
The Shapley value is a prominent tool for interpreting black-box machine learning models
thanks to its strong theoretical foundation. However, for models with structured inputs, such …
thanks to its strong theoretical foundation. However, for models with structured inputs, such …
Factorized explainer for graph neural networks
Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. To open the black-box of these deep learning models, post …
learn from graph-structured data. To open the black-box of these deep learning models, post …