Summary Interpretable Graph Neural Networks for Tabular Data arxiv.org
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IGNNet is a Graph Neural Network (GNN) approach that focuses on interpretability of tabular data for legal, ethical, and user-related purposes.
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Key Points
- The article discusses the use of Graph Neural Networks (GNNs) for handling tabular data and proposes a new approach called IGNNet that produces interpretable models.
- Interpretable models are important for legal and ethical considerations and for user trust.
- The key steps in a Graph Neural Network (GNN) for graph classification are message passing and graph pooling.
- IGNNet is designed for tabular data analysis, with a focus on interpretability and robustness to adversarial attacks and incomplete data.
- The OGNNet model uses a white box classifier to determine the predictive performance loss when squashing multidimensional node representations into scalar values.
- IGNNet was evaluated for its explainability and predictive performance, generating explanations aligned with Shapley values without additional computational cost.
- TabGNN is a multiplex Graph Neural Network designed for predicting tabular data and generating realistic counterfactuals.
- The computed feature scores by IGNNet can be used to explain predictions made by the model.
Summaries
26 word summary
The article introduces IGNNet, a Graph Neural Network (GNN) approach for interpreting tabular data, emphasizing the importance of interpretable models for legal, ethical, and user-related reasons.
41 word summary
The article discusses the use of Graph Neural Networks (GNNs) for handling tabular data and proposes a new approach called IGNNet that produces interpretable models. The authors argue that interpretable models are important for legal and ethical considerations and for user
435 word summary
The article discusses the use of Graph Neural Networks (GNNs) for handling tabular data and proposes a new approach called IGNNet that produces interpretable models. The authors argue that interpretable models are important for legal and ethical considerations and for user
In this document, the authors discuss self-explaining neural networks and their relation to model-agnostic explanation techniques. They provide examples of different approaches to generating self-explaining neural networks, including methods for text classification and counterfactual examples. The
The key steps in a Graph Neural Network (GNN) for graph classification are message passing and graph pooling. In the message passing phase, each node passes a message to its neighboring nodes and aggregates the information. The node representation is updated using a neural
Interpretable Graph Neural Networks (IGNNet) are proposed for tabular data analysis, with a focus on interpretability and robustness to adversarial attacks and incomplete data. The readout function is designed to produce an interpretable output layer, but
The study introduces the OGNNet model, which uses a white box classifier instead of a black box to determine the predictive performance loss when squashing multidimensional node representations into scalar values. The model is tested on 35 publicly available datasets, with each
The authors conducted experiments to evaluate the performance and interpretability of their proposed algorithm, IGNNet, for tabular data classification. They used oversampling for binary datasets and calculated the area under the ROC curve (AUC) to measure predictive performance. For
In a large-scale empirical investigation, the Interpretable Graph Neural Network (IGNNet) was evaluated for its explainability and predictive performance. The results showed that IGNNet generated explanations with feature scores aligned with Shapley values without additional computational cost.
This text excerpt contains a list of references and citations from various papers and conferences related to graph neural networks and tabular data analysis. The references include papers on interpretable tabular learning, logistic regression, random forests, XGBoost, Shapley
TabGNN is a multiplex Graph Neural Network designed for predicting tabular data. It is a self-explaining model that generates realistic counterfactuals. The Expressive Power of Graph Neural Networks is explored, emphasizing their ability to capture complex
The document includes a list of references to relevant papers on interpretable graph neural networks for tabular data. The main focus of the paper is on the explanation of predictions made by IGNNet. The authors demonstrate how the computed feature scores by IGNNet can
This summary presents the key points from the excerpted text.
The authors demonstrate the feature scores for predictions made by IGNNet using examples from the Adult dataset and the Churn dataset. The feature scores are sorted and displayed, showing the top 10